Search for Association Rules Between Song Genres and Their Popularity
Teo Le Provost
2026-01-29
# Imports
library(dplyr) # Data manipulation
library(tidyr) # Data tidying
library(stringr) # String manipulation
library(arules) # Association rules mining
library(arulesViz) # Visualization of association rules
library(gmodels) # CrossTable for crosstab analysis# Set language to English and data repository path
Sys.setenv(LANGUAGE='en')
metadata_dir <- "/Users/teo/Datasets/fma/data"Data subset description (FMA)
This project uses the Free Music Archive (FMA) dataset, specifically
the tracks.csv file containing per-track metadata
including:
- track_id: Unique identifier
- 53 features organized in groups:
- Album features (13): comments, date_created, date_released, engineer, favorites, etc.
- Artist features (17): bio, comments, favorites, location, name, etc.
- Track features (23): bit_rate, comments, composer, date_created, duration, favorites, genre, language_code, license, listens, etc.
Project Goal: Association rules mining aims to
discover frequent co-occurrences of discrete items within transactions.
The original tracks.csv dataset contains a mixture of
categorical variables and continuous popularity indicators (e.g., number
of listens or interest).
To make association rule mining applicable and interpretable, we transform selected continuous variables into discrete categories. This allows us to investigate whether certain musical genres tend to be associated with higher or lower popularity levels, exploring multiple dimensions of visibility without assuming linear or metric relationships between variables.
More information of this dataset can be found on the following links: - Github repositoy: https://github.com/mdeff/fma?tab=readme-ov-file - Website: https://freemusicarchive.org/ - Codebook paper: https://arxiv.org/abs/1612.01840
Data Preparation
Data Loading and Subset Selection
tracks_raw <- read.csv(file.path(metadata_dir, "tracks.csv"), sep = ",")
# Display dimensions
cat("Dataset dimensions:", nrow(tracks_raw), "observations of", ncol(tracks_raw), "variables\n")## Dataset dimensions: 106576 observations of 53 variablesHeader Cleaning
The CSV has a complex multi-level header structure that needs to be consolidated.
# Get column names from each row
header_group <- colnames(tracks_raw)
header_group <- gsub("\\.\\d+$", "", header_group)
header_feature_1 <- as.character(tracks_raw[1, ])
header_feature_2 <- as.character(tracks_raw[2, ])
# Replace empty strings to manipulate them more easily after
header_group[header_group == "" | header_group == "X"] <- NA
header_feature_1[header_feature_1 == ""] <- NA
header_feature_2[header_feature_2 == ""] <- NA
# Helper function to concatenate non-NA values
clean_concat <- function(h1, h2, h3) {
if (is.na(h1) & is.na(h2)) {
return(h3)
} else if (is.na(h3)) {
return(paste(h1, h2, sep = "."))
} else {
return(paste(h1, h2, h3, sep = "."))
}
}
# Combine the three headers into one
new_colnames <- mapply(
clean_concat,
header_group,
header_feature_1,
header_feature_2,
SIMPLIFY = TRUE
)
# Clean and assign new column names
tracks_clean <- tracks_raw[-c(1, 2), ]
colnames(tracks_clean) <- new_colnames
# Extract the line with the column features
head(tracks_clean)## track_id album.comments album.date_created album.date_released
## 3 2 0 2008-11-26 01:44:45 2009-01-05 00:00:00
## 4 3 0 2008-11-26 01:44:45 2009-01-05 00:00:00
## 5 5 0 2008-11-26 01:44:45 2009-01-05 00:00:00
## 6 10 0 2008-11-26 01:45:08 2008-02-06 00:00:00
## 7 20 0 2008-11-26 01:45:05 2009-01-06 00:00:00
## 8 26 0 2008-11-26 01:45:05 2009-01-06 00:00:00
## album.engineer album.favorites album.id
## 3 4 1
## 4 4 1
## 5 4 1
## 6 4 6
## 7 2 4
## 8 2 4
## album.information album.listens album.producer
## 3 <p></p> 6073
## 4 <p></p> 6073
## 5 <p></p> 6073
## 6 47632
## 7 <p>Â "spiritual songs" from Nicky Cook</p> 2710
## 8 <p>Â "spiritual songs" from Nicky Cook</p> 2710
## album.tags album.title album.tracks album.type
## 3 [] AWOL - A Way Of Life 7 Album
## 4 [] AWOL - A Way Of Life 7 Album
## 5 [] AWOL - A Way Of Life 7 Album
## 6 [] Constant Hitmaker 2 Album
## 7 [] Niris 13 Album
## 8 [] Niris 13 Album
## artist.active_year_begin artist.active_year_end
## 3 2006-01-01 00:00:00
## 4 2006-01-01 00:00:00
## 5 2006-01-01 00:00:00
## 6
## 7 1990-01-01 00:00:00 2011-01-01 00:00:00
## 8 1990-01-01 00:00:00 2011-01-01 00:00:00
## artist.associated_labels
## 3
## 4
## 5
## 6 Mexican Summer, Richie Records, Woodsist, Skulltones
## 7
## 8
## artist.bio
## 3 <p>A Way Of Life, A Collective of Hip-Hop from NJ...................</p>
## 4 <p>A Way Of Life, A Collective of Hip-Hop from NJ...................</p>
## 5 <p>A Way Of Life, A Collective of Hip-Hop from NJ...................</p>
## 6 <p><span style="font-family:Verdana, Geneva, Arial;font-size:xx-small;">"Philly's Constant Hitmaker" Kurt Vile plays psychedelic experimental pop infused with a classic, folksy directness. 2008's Gulcher Records release <i>Constant Hitmaker</i> more than lives up to the name. Vile's a consistent live performer who's shared the stage with a range of sonic adventurers and avant pop-stars including Sonic Youth, Big Star, Blues Control, Magik Markers, and Ariel Pink's Haunted Graffiti.</span></p><p><span style="font-family:Verdana, Geneva, Arial;font-size:xx-small;">After a slew of limited releases on labels like Mexican Summer, Richie Records, Woodsist, and Skulltones,</span><span style="font-family:Verdana, Geneva, Arial;font-size:xx-small;"> Kurt Vile signed to Matador Records who released his first proper full-length (the Gulcher release was more of a "greatest hits"), <a title="Childish Prodigy" href="http://www.matadorrecords.com/store/index.php?catalog_id=387">Childish Prodigy</a>.<br /></span></p>
## 7 <p>Songs written by: Nicky Cook</p>\n<p>VOCALS, CAJON AND TABLA+ UKELELE</p>
## 8 <p>Songs written by: Nicky Cook</p>\n<p>VOCALS, CAJON AND TABLA+ UKELELE</p>
## artist.comments artist.date_created artist.favorites artist.id
## 3 0 2008-11-26 01:42:32 9 1
## 4 0 2008-11-26 01:42:32 9 1
## 5 0 2008-11-26 01:42:32 9 1
## 6 3 2008-11-26 01:42:55 74 6
## 7 2 2008-11-26 01:42:52 10 4
## 8 2 2008-11-26 01:42:52 10 4
## artist.latitude artist.location artist.longitude
## 3 40.0583238 New Jersey -74.4056612
## 4 40.0583238 New Jersey -74.4056612
## 5 40.0583238 New Jersey -74.4056612
## 6
## 7 51.895927 Colchester England 0.891874
## 8 51.895927 Colchester England 0.891874
## artist.members
## 3 Sajje Morocco,Brownbum,ZawidaGod,Custodian of Records,Zooberelli the Don,F.A.H,MadSicka,Damien Omenicci..and a van load more...
## 4 Sajje Morocco,Brownbum,ZawidaGod,Custodian of Records,Zooberelli the Don,F.A.H,MadSicka,Damien Omenicci..and a van load more...
## 5 Sajje Morocco,Brownbum,ZawidaGod,Custodian of Records,Zooberelli the Don,F.A.H,MadSicka,Damien Omenicci..and a van load more...
## 6 Kurt Vile, the Violators
## 7 Nicky Cook\n
## 8 Nicky Cook\n
## artist.name
## 3 AWOL
## 4 AWOL
## 5 AWOL
## 6 Kurt Vile
## 7 Nicky Cook
## 8 Nicky Cook
## artist.related_projects
## 3 The list of past projects is 2 long but every1 and every style from Tabby Bonet 2 M.O.P..Azillion Records Flagship trackmaster DJ BrownBum is a beat Wizard.....A-2-Z..illion....(right now working with JerseyBlock Ent)
## 4 The list of past projects is 2 long but every1 and every style from Tabby Bonet 2 M.O.P..Azillion Records Flagship trackmaster DJ BrownBum is a beat Wizard.....A-2-Z..illion....(right now working with JerseyBlock Ent)
## 5 The list of past projects is 2 long but every1 and every style from Tabby Bonet 2 M.O.P..Azillion Records Flagship trackmaster DJ BrownBum is a beat Wizard.....A-2-Z..illion....(right now working with JerseyBlock Ent)
## 6
## 7
## 8
## artist.tags
## 3 ['awol']
## 4 ['awol']
## 5 ['awol']
## 6 ['philly', 'kurt vile']
## 7 ['instrumentals', 'experimental pop', 'post punk', 'experimental rock', 'soundtracks', 'lo fi', 'nicky', 'impro', 'cook', 'nicky andrews', 'ukelele', 'nicky cook', 'singer songwriter', 'tabla']
## 8 ['instrumentals', 'experimental pop', 'post punk', 'experimental rock', 'soundtracks', 'lo fi', 'nicky', 'impro', 'cook', 'nicky andrews', 'ukelele', 'nicky cook', 'singer songwriter', 'tabla']
## artist.website artist.wikipedia_page set.split
## 3 http://www.AzillionRecords.blogspot.com training
## 4 http://www.AzillionRecords.blogspot.com training
## 5 http://www.AzillionRecords.blogspot.com training
## 6 http://kurtvile.com training
## 7 training
## 8 training
## set.subset track.bit_rate track.comments track.composer track.date_created
## 3 small 256000 0 2008-11-26 01:48:12
## 4 medium 256000 0 2008-11-26 01:48:14
## 5 small 256000 0 2008-11-26 01:48:20
## 6 small 192000 0 Kurt Vile 2008-11-25 17:49:06
## 7 large 256000 0 2008-11-26 01:48:56
## 8 large 256000 0 2008-11-26 01:49:05
## track.date_recorded track.duration track.favorites track.genre_top
## 3 2008-11-26 00:00:00 168 2 Hip-Hop
## 4 2008-11-26 00:00:00 237 1 Hip-Hop
## 5 2008-11-26 00:00:00 206 6 Hip-Hop
## 6 2008-11-26 00:00:00 161 178 Pop
## 7 2008-01-01 00:00:00 311 0
## 8 2008-01-01 00:00:00 181 0
## track.genres track.genres_all track.information track.interest
## 3 [21] [21] 4656
## 4 [21] [21] 1470
## 5 [21] [21] 1933
## 6 [10] [10] 54881
## 7 [76, 103] [17, 10, 76, 103] 978
## 8 [76, 103] [17, 10, 76, 103] 1060
## track.language_code
## 3 en
## 4 en
## 5 en
## 6 en
## 7 en
## 8 en
## track.license
## 3 Attribution-NonCommercial-ShareAlike 3.0 International
## 4 Attribution-NonCommercial-ShareAlike 3.0 International
## 5 Attribution-NonCommercial-ShareAlike 3.0 International
## 6 Attribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International
## 7 Attribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International
## 8 Attribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International
## track.listens track.lyricist track.number track.publisher track.tags
## 3 1293 3 []
## 4 514 4 []
## 5 1151 6 []
## 6 50135 1 []
## 7 361 3 []
## 8 193 4 []
## track.title
## 3 Food
## 4 Electric Ave
## 5 This World
## 6 Freeway
## 7 Spiritual Level
## 8 Where is your Love?Feature Selection
We focus on features that can provide meaningful transaction items:
Categorical variables: -
track.genre_all - track.language_code
Continuous variables to discretize: -
track.listens: Number of listens
tracks_sel <- tracks_clean %>%
select(
track.genres_all,
track.language_code,
track.listens
)
# Remove rows with empty values
tracks_sel <- tracks_sel %>%
mutate(
track.genres_all = na_if(track.genres_all, ""),
track.language_code = na_if(track.language_code, ""),
track.listens = na_if(track.listens, "")
)
tracks_sel <- tracks_sel %>%
filter(
!is.na(track.genres_all),
!is.na(track.language_code),
!is.na(track.listens)
)
head(tracks_sel)## track.genres_all track.language_code track.listens
## 1 [21] en 1293
## 2 [21] en 514
## 3 [21] en 1151
## 4 [10] en 50135
## 5 [17, 10, 76, 103] en 361
## 6 [17, 10, 76, 103] en 193Only tracks with complete information for the selected variables are retained to avoid ambiguous transactions.
Data Type Conversion
# Convert to appropriate data types
tracks_sel <- tracks_sel %>%
mutate(
track.listens = as.numeric(track.listens)
)
str(tracks_sel)## 'data.frame': 15024 obs. of 3 variables:
## $ track.genres_all : chr "[21]" "[21]" "[21]" "[10]" ...
## $ track.language_code: chr "en" "en" "en" "en" ...
## $ track.listens : num 1293 514 1151 50135 361 ...Discretization of Continuous Variables
Before discretizing, we examine the distribution of the continuous popularity indicators.
summary(tracks_sel$track.listens)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5 210 489 1423 1182 325940The data shows the wrong type (character instead of factors). We need to convert the continuous variables to numeric and the categorical ones to factors.
Here, we discretize the continuous popularity indicators into three categories (low, medium, high) based on quantiles. This allows us to capture relative popularity levels without assuming specific thresholds.
tracks_disc <- tracks_sel %>%
mutate(
listens = cut(
track.listens,
breaks = quantile(track.listens, probs = c(0, 0.33, 0.66, 1)),
labels = c("low", "medium", "high"),
include.lowest = TRUE)) %>%
select(-track.listens) %>%
rename(genres = track.genres_all,
language = track.language_code)
head(tracks_disc)## genres language listens
## 1 [21] en high
## 2 [21] en medium
## 3 [21] en high
## 4 [10] en high
## 5 [17, 10, 76, 103] en medium
## 6 [17, 10, 76, 103] en lowSeparate the genres and find their corresponding name
tracks_disc <- tracks_disc %>%
mutate(
genres = na_if(genres, ""),
genres = str_remove_all(genres, "\\[|\\]")
) %>%
filter(!is.na(genres)) %>%
separate_rows(genres, sep = ",") %>%
mutate(
genre_id = as.integer(str_trim(genres))
)genres <- read.csv(file.path(metadata_dir, "genres.csv"), sep = ",") %>%
select(genre_id, title)tracks_disc <- tracks_disc %>%
left_join(genres, by = "genre_id") %>%
filter(!is.na(title)) %>%
mutate(genre = as.factor(title),
language = as.factor(language)) %>%
select(-genres, -genre_id, -title)
tracks_disc## # A tibble: 40,793 × 3
## language listens genre
## <fct> <fct> <fct>
## 1 en high Hip-Hop
## 2 en medium Hip-Hop
## 3 en high Hip-Hop
## 4 en high Pop
## 5 en medium Folk
## 6 en medium Pop
## 7 en medium Experimental Pop
## 8 en medium Singer-Songwriter
## 9 en low Folk
## 10 en low Pop
## # ℹ 40,783 more rowsTransaction Creation
One transaction = a list of textual items, not a list of numerical values.
tracks_disc## # A tibble: 40,793 × 3
## language listens genre
## <fct> <fct> <fct>
## 1 en high Hip-Hop
## 2 en medium Hip-Hop
## 3 en high Hip-Hop
## 4 en high Pop
## 5 en medium Folk
## 6 en medium Pop
## 7 en medium Experimental Pop
## 8 en medium Singer-Songwriter
## 9 en low Folk
## 10 en low Pop
## # ℹ 40,783 more rowsData Inspection
tracks_basket <- as(tracks_disc, "transactions")
inspect(tracks_basket[1:3])## items transactionID
## [1] {language=en, listens=high, genre=Hip-Hop} 1
## [2] {language=en, listens=medium, genre=Hip-Hop} 2
## [3] {language=en, listens=high, genre=Hip-Hop} 3Item Frequency Analysis
item_freq <- itemFrequency(tracks_basket)
head(item_freq, decreasing = TRUE)## language=ar language=az language=bg language=bm language=cs language=de
## 8.334763e-04 9.805604e-05 3.431961e-04 7.354203e-05 3.922242e-04 4.755718e-03The most frequent items in the dataset are specific language-genre combinations, with English language tracks being dominant.
item_freq <- base::sort(item_freq, decreasing=T)[0:20]
par(mar = c(10, 4, 2, 1)) # plus de place en bas
barplot(
item_freq,
las = 2,
cex.names = 0.8
)
Cross-tabulation Analysis
CrossTable(tracks_disc$genre, tracks_disc$listens)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40793
##
##
## | tracks_disc$listens
## tracks_disc$genre | low | medium | high | Row Total |
## -------------------------|-----------|-----------|-----------|-----------|
## 20th Century Classical | 0 | 4 | 56 | 60 |
## | 17.494 | 12.905 | 50.418 | |
## | 0.000 | 0.067 | 0.933 | 0.001 |
## | 0.000 | 0.000 | 0.004 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## African | 0 | 0 | 57 | 57 |
## | 16.619 | 19.104 | 59.979 | |
## | 0.000 | 0.000 | 1.000 | 0.001 |
## | 0.000 | 0.000 | 0.004 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Afrobeat | 0 | 0 | 5 | 5 |
## | 1.458 | 1.676 | 5.261 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Alternative Hip-Hop | 29 | 34 | 57 | 120 |
## | 1.025 | 0.962 | 3.327 | |
## | 0.242 | 0.283 | 0.475 | 0.003 |
## | 0.002 | 0.002 | 0.004 | |
## | 0.001 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Ambient | 10 | 103 | 173 | 286 |
## | 64.588 | 0.533 | 41.105 | |
## | 0.035 | 0.360 | 0.605 | 0.007 |
## | 0.001 | 0.008 | 0.011 | |
## | 0.000 | 0.003 | 0.004 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Ambient Electronic | 61 | 191 | 125 | 377 |
## | 21.773 | 33.075 | 1.757 | |
## | 0.162 | 0.507 | 0.332 | 0.009 |
## | 0.005 | 0.014 | 0.008 | |
## | 0.001 | 0.005 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Americana | 2 | 40 | 109 | 151 |
## | 40.118 | 2.224 | 49.153 | |
## | 0.013 | 0.265 | 0.722 | 0.004 |
## | 0.000 | 0.003 | 0.007 | |
## | 0.000 | 0.001 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Asia-Far East | 0 | 0 | 29 | 29 |
## | 8.456 | 9.720 | 30.516 | |
## | 0.000 | 0.000 | 1.000 | 0.001 |
## | 0.000 | 0.000 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Audio Collage | 127 | 80 | 80 | 287 |
## | 22.426 | 2.725 | 6.870 | |
## | 0.443 | 0.279 | 0.279 | 0.007 |
## | 0.011 | 0.006 | 0.005 | |
## | 0.003 | 0.002 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Avant-Garde | 516 | 329 | 291 | 1136 |
## | 103.080 | 7.030 | 41.741 | |
## | 0.454 | 0.290 | 0.256 | 0.028 |
## | 0.043 | 0.024 | 0.019 | |
## | 0.013 | 0.008 | 0.007 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Balkan | 0 | 22 | 61 | 83 |
## | 24.200 | 1.217 | 29.085 | |
## | 0.000 | 0.265 | 0.735 | 0.002 |
## | 0.000 | 0.002 | 0.004 | |
## | 0.000 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Banter | 0 | 0 | 4 | 4 |
## | 1.166 | 1.341 | 4.209 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Big Band/Swing | 0 | 0 | 19 | 19 |
## | 5.540 | 6.368 | 19.993 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Bigbeat | 0 | 0 | 11 | 11 |
## | 3.207 | 3.687 | 11.575 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Black-Metal | 0 | 8 | 12 | 20 |
## | 5.831 | 0.251 | 2.754 | |
## | 0.000 | 0.400 | 0.600 | 0.000 |
## | 0.000 | 0.001 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Bluegrass | 0 | 0 | 87 | 87 |
## | 25.367 | 29.159 | 91.547 | |
## | 0.000 | 0.000 | 1.000 | 0.002 |
## | 0.000 | 0.000 | 0.006 | |
## | 0.000 | 0.000 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Blues | 0 | 12 | 150 | 162 |
## | 47.234 | 32.947 | 132.553 | |
## | 0.000 | 0.074 | 0.926 | 0.004 |
## | 0.000 | 0.001 | 0.010 | |
## | 0.000 | 0.000 | 0.004 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Brazilian | 0 | 3 | 72 | 75 |
## | 21.868 | 19.495 | 69.167 | |
## | 0.000 | 0.040 | 0.960 | 0.002 |
## | 0.000 | 0.000 | 0.005 | |
## | 0.000 | 0.000 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Breakbeat | 4 | 2 | 1 | 7 |
## | 1.880 | 0.051 | 0.996 | |
## | 0.571 | 0.286 | 0.143 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Breakcore - Hard | 9 | 36 | 32 | 77 |
## | 8.059 | 4.026 | 0.369 | |
## | 0.117 | 0.468 | 0.416 | 0.002 |
## | 0.001 | 0.003 | 0.002 | |
## | 0.000 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## British Folk | 0 | 3 | 15 | 18 |
## | 5.248 | 1.525 | 10.206 | |
## | 0.000 | 0.167 | 0.833 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Celtic | 0 | 0 | 15 | 15 |
## | 4.374 | 5.027 | 15.784 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Chill-out | 0 | 0 | 11 | 11 |
## | 3.207 | 3.687 | 11.575 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Chip Music | 18 | 117 | 132 | 267 |
## | 46.011 | 8.459 | 10.491 | |
## | 0.067 | 0.438 | 0.494 | 0.007 |
## | 0.002 | 0.009 | 0.009 | |
## | 0.000 | 0.003 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Chiptune | 0 | 45 | 55 | 100 |
## | 29.157 | 3.935 | 8.367 | |
## | 0.000 | 0.450 | 0.550 | 0.002 |
## | 0.000 | 0.003 | 0.004 | |
## | 0.000 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Choral Music | 0 | 16 | 30 | 46 |
## | 13.412 | 0.022 | 9.586 | |
## | 0.000 | 0.348 | 0.652 | 0.001 |
## | 0.000 | 0.001 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Christmas | 0 | 0 | 4 | 4 |
## | 1.166 | 1.341 | 4.209 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Classical | 0 | 28 | 336 | 364 |
## | 106.131 | 72.423 | 294.771 | |
## | 0.000 | 0.077 | 0.923 | 0.009 |
## | 0.000 | 0.002 | 0.022 | |
## | 0.000 | 0.001 | 0.008 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Comedy | 0 | 13 | 42 | 55 |
## | 16.036 | 1.602 | 22.453 | |
## | 0.000 | 0.236 | 0.764 | 0.001 |
## | 0.000 | 0.001 | 0.003 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Compilation | 36 | 35 | 14 | 85 |
## | 5.076 | 1.488 | 9.906 | |
## | 0.424 | 0.412 | 0.165 | 0.002 |
## | 0.003 | 0.003 | 0.001 | |
## | 0.001 | 0.001 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Composed Music | 0 | 4 | 75 | 79 |
## | 23.034 | 19.082 | 70.240 | |
## | 0.000 | 0.051 | 0.949 | 0.002 |
## | 0.000 | 0.000 | 0.005 | |
## | 0.000 | 0.000 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Contemporary Classical | 0 | 0 | 4 | 4 |
## | 1.166 | 1.341 | 4.209 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Country | 2 | 70 | 212 | 284 |
## | 78.854 | 6.663 | 105.970 | |
## | 0.007 | 0.246 | 0.746 | 0.007 |
## | 0.000 | 0.005 | 0.014 | |
## | 0.000 | 0.002 | 0.005 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Cumbia | 0 | 0 | 1 | 1 |
## | 0.292 | 0.335 | 1.052 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Dance | 3 | 116 | 127 | 246 |
## | 65.852 | 13.654 | 13.474 | |
## | 0.012 | 0.472 | 0.516 | 0.006 |
## | 0.000 | 0.008 | 0.008 | |
## | 0.000 | 0.003 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Death-Metal | 0 | 0 | 5 | 5 |
## | 1.458 | 1.676 | 5.261 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Deep Funk | 0 | 0 | 1 | 1 |
## | 0.292 | 0.335 | 1.052 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Disco | 0 | 31 | 67 | 98 |
## | 28.574 | 0.104 | 25.295 | |
## | 0.000 | 0.316 | 0.684 | 0.002 |
## | 0.000 | 0.002 | 0.004 | |
## | 0.000 | 0.001 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Downtempo | 0 | 1 | 1 | 2 |
## | 0.583 | 0.162 | 0.086 | |
## | 0.000 | 0.500 | 0.500 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Drone | 19 | 86 | 62 | 167 |
## | 18.106 | 16.111 | 0.002 | |
## | 0.114 | 0.515 | 0.371 | 0.004 |
## | 0.002 | 0.006 | 0.004 | |
## | 0.000 | 0.002 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Drum & Bass | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Dubstep | 0 | 19 | 52 | 71 |
## | 20.701 | 0.967 | 24.531 | |
## | 0.000 | 0.268 | 0.732 | 0.002 |
## | 0.000 | 0.001 | 0.003 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Easy Listening | 3 | 8 | 48 | 59 |
## | 11.726 | 7.011 | 30.640 | |
## | 0.051 | 0.136 | 0.814 | 0.001 |
## | 0.000 | 0.001 | 0.003 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Easy Listening: Vocal | 2 | 2 | 0 | 4 |
## | 0.596 | 0.324 | 1.493 | |
## | 0.500 | 0.500 | 0.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Electro-Punk | 22 | 83 | 37 | 142 |
## | 9.093 | 26.343 | 4.833 | |
## | 0.155 | 0.585 | 0.261 | 0.003 |
## | 0.002 | 0.006 | 0.002 | |
## | 0.001 | 0.002 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Electroacoustic | 100 | 99 | 117 | 316 |
## | 0.671 | 0.451 | 0.008 | |
## | 0.316 | 0.313 | 0.370 | 0.008 |
## | 0.008 | 0.007 | 0.008 | |
## | 0.002 | 0.002 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Electronic | 811 | 1297 | 1277 | 3385 |
## | 31.372 | 23.275 | 0.143 | |
## | 0.240 | 0.383 | 0.377 | 0.083 |
## | 0.068 | 0.095 | 0.084 | |
## | 0.020 | 0.032 | 0.031 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Europe | 1 | 18 | 94 | 113 |
## | 30.978 | 10.428 | 63.663 | |
## | 0.009 | 0.159 | 0.832 | 0.003 |
## | 0.000 | 0.001 | 0.006 | |
## | 0.000 | 0.000 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Experimental | 1838 | 1316 | 1176 | 4330 |
## | 262.341 | 12.600 | 119.934 | |
## | 0.424 | 0.304 | 0.272 | 0.106 |
## | 0.155 | 0.096 | 0.077 | |
## | 0.045 | 0.032 | 0.029 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Experimental Pop | 296 | 463 | 315 | 1074 |
## | 0.939 | 29.498 | 18.405 | |
## | 0.276 | 0.431 | 0.293 | 0.026 |
## | 0.025 | 0.034 | 0.021 | |
## | 0.007 | 0.011 | 0.008 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Fado | 0 | 0 | 1 | 1 |
## | 0.292 | 0.335 | 1.052 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Field Recordings | 182 | 57 | 32 | 271 |
## | 134.225 | 12.598 | 47.280 | |
## | 0.672 | 0.210 | 0.118 | 0.007 |
## | 0.015 | 0.004 | 0.002 | |
## | 0.004 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Flamenco | 0 | 0 | 1 | 1 |
## | 0.292 | 0.335 | 1.052 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Folk | 444 | 710 | 829 | 1983 |
## | 31.141 | 3.099 | 10.652 | |
## | 0.224 | 0.358 | 0.418 | 0.049 |
## | 0.037 | 0.052 | 0.054 | |
## | 0.011 | 0.017 | 0.020 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Freak-Folk | 10 | 19 | 23 | 52 |
## | 1.757 | 0.142 | 0.664 | |
## | 0.192 | 0.365 | 0.442 | 0.001 |
## | 0.001 | 0.001 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Free-Folk | 20 | 45 | 27 | 92 |
## | 1.736 | 6.508 | 1.569 | |
## | 0.217 | 0.489 | 0.293 | 0.002 |
## | 0.002 | 0.003 | 0.002 | |
## | 0.000 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Free-Jazz | 26 | 89 | 135 | 250 |
## | 30.166 | 0.324 | 18.617 | |
## | 0.104 | 0.356 | 0.540 | 0.006 |
## | 0.002 | 0.007 | 0.009 | |
## | 0.001 | 0.002 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## French | 0 | 0 | 11 | 11 |
## | 3.207 | 3.687 | 11.575 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Funk | 0 | 1 | 43 | 44 |
## | 12.829 | 12.815 | 43.003 | |
## | 0.000 | 0.023 | 0.977 | 0.001 |
## | 0.000 | 0.000 | 0.003 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Garage | 177 | 169 | 130 | 476 |
## | 10.521 | 0.562 | 12.794 | |
## | 0.372 | 0.355 | 0.273 | 0.012 |
## | 0.015 | 0.012 | 0.009 | |
## | 0.004 | 0.004 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Glitch | 15 | 59 | 34 | 108 |
## | 8.635 | 14.366 | 0.989 | |
## | 0.139 | 0.546 | 0.315 | 0.003 |
## | 0.001 | 0.004 | 0.002 | |
## | 0.000 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Goth | 0 | 7 | 15 | 22 |
## | 6.415 | 0.019 | 5.611 | |
## | 0.000 | 0.318 | 0.682 | 0.001 |
## | 0.000 | 0.001 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Grindcore | 1 | 6 | 3 | 10 |
## | 1.259 | 2.093 | 0.144 | |
## | 0.100 | 0.600 | 0.300 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Hardcore | 67 | 51 | 44 | 162 |
## | 8.271 | 0.200 | 4.486 | |
## | 0.414 | 0.315 | 0.272 | 0.004 |
## | 0.006 | 0.004 | 0.003 | |
## | 0.002 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Hip-Hop | 233 | 285 | 371 | 889 |
## | 2.649 | 0.563 | 4.621 | |
## | 0.262 | 0.321 | 0.417 | 0.022 |
## | 0.020 | 0.021 | 0.024 | |
## | 0.006 | 0.007 | 0.009 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Hip-Hop Beats | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Holiday | 1 | 5 | 27 | 33 |
## | 7.726 | 3.321 | 17.499 | |
## | 0.030 | 0.152 | 0.818 | 0.001 |
## | 0.000 | 0.000 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## House | 0 | 8 | 69 | 77 |
## | 22.451 | 12.287 | 56.387 | |
## | 0.000 | 0.104 | 0.896 | 0.002 |
## | 0.000 | 0.001 | 0.005 | |
## | 0.000 | 0.000 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## IDM | 8 | 4 | 21 | 33 |
## | 0.273 | 4.507 | 6.119 | |
## | 0.242 | 0.121 | 0.636 | 0.001 |
## | 0.001 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Improv | 203 | 152 | 110 | 465 |
## | 33.526 | 0.095 | 23.284 | |
## | 0.437 | 0.327 | 0.237 | 0.011 |
## | 0.017 | 0.011 | 0.007 | |
## | 0.005 | 0.004 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Indian | 0 | 0 | 17 | 17 |
## | 4.957 | 5.698 | 17.889 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Indie-Rock | 289 | 359 | 236 | 884 |
## | 3.789 | 13.278 | 26.764 | |
## | 0.327 | 0.406 | 0.267 | 0.022 |
## | 0.024 | 0.026 | 0.015 | |
## | 0.007 | 0.009 | 0.006 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Industrial | 24 | 42 | 47 | 113 |
## | 2.430 | 0.450 | 0.551 | |
## | 0.212 | 0.372 | 0.416 | 0.003 |
## | 0.002 | 0.003 | 0.003 | |
## | 0.001 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Instrumental | 46 | 142 | 360 | 548 |
## | 81.023 | 9.452 | 118.126 | |
## | 0.084 | 0.259 | 0.657 | 0.013 |
## | 0.004 | 0.010 | 0.024 | |
## | 0.001 | 0.003 | 0.009 | |
## -------------------------|-----------|-----------|-----------|-----------|
## International | 19 | 101 | 575 | 695 |
## | 166.422 | 74.727 | 383.874 | |
## | 0.027 | 0.145 | 0.827 | 0.017 |
## | 0.002 | 0.007 | 0.038 | |
## | 0.000 | 0.002 | 0.014 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Interview | 25 | 11 | 4 | 40 |
## | 15.252 | 0.432 | 8.003 | |
## | 0.625 | 0.275 | 0.100 | 0.001 |
## | 0.002 | 0.001 | 0.000 | |
## | 0.001 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Jazz | 69 | 216 | 330 | 615 |
## | 67.866 | 0.474 | 43.941 | |
## | 0.112 | 0.351 | 0.537 | 0.015 |
## | 0.006 | 0.016 | 0.022 | |
## | 0.002 | 0.005 | 0.008 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Jazz: Out | 18 | 38 | 40 | 96 |
## | 3.566 | 1.055 | 0.484 | |
## | 0.188 | 0.396 | 0.417 | 0.002 |
## | 0.002 | 0.003 | 0.003 | |
## | 0.000 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Jazz: Vocal | 0 | 0 | 49 | 49 |
## | 14.287 | 16.423 | 51.561 | |
## | 0.000 | 0.000 | 1.000 | 0.001 |
## | 0.000 | 0.000 | 0.003 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Kid-Friendly | 0 | 8 | 7 | 15 |
## | 4.374 | 1.758 | 0.350 | |
## | 0.000 | 0.533 | 0.467 | 0.000 |
## | 0.000 | 0.001 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Krautrock | 74 | 30 | 30 | 134 |
## | 31.228 | 4.951 | 8.012 | |
## | 0.552 | 0.224 | 0.224 | 0.003 |
## | 0.006 | 0.002 | 0.002 | |
## | 0.002 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Latin | 0 | 0 | 13 | 13 |
## | 3.790 | 4.357 | 13.679 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Latin America | 3 | 7 | 69 | 79 |
## | 17.425 | 14.328 | 52.940 | |
## | 0.038 | 0.089 | 0.873 | 0.002 |
## | 0.000 | 0.001 | 0.005 | |
## | 0.000 | 0.000 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Lo-Fi | 432 | 279 | 238 | 949 |
## | 87.164 | 4.797 | 38.142 | |
## | 0.455 | 0.294 | 0.251 | 0.023 |
## | 0.036 | 0.020 | 0.016 | |
## | 0.011 | 0.007 | 0.006 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Loud-Rock | 172 | 208 | 106 | 486 |
## | 6.478 | 12.495 | 31.348 | |
## | 0.354 | 0.428 | 0.218 | 0.012 |
## | 0.014 | 0.015 | 0.007 | |
## | 0.004 | 0.005 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Lounge | 1 | 6 | 46 | 53 |
## | 13.518 | 7.790 | 34.741 | |
## | 0.019 | 0.113 | 0.868 | 0.001 |
## | 0.000 | 0.000 | 0.003 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Metal | 24 | 59 | 72 | 155 |
## | 9.939 | 0.957 | 3.457 | |
## | 0.155 | 0.381 | 0.465 | 0.004 |
## | 0.002 | 0.004 | 0.005 | |
## | 0.001 | 0.001 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Middle East | 0 | 0 | 45 | 45 |
## | 13.121 | 15.082 | 47.352 | |
## | 0.000 | 0.000 | 1.000 | 0.001 |
## | 0.000 | 0.000 | 0.003 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Minimal Electronic | 5 | 75 | 133 | 213 |
## | 52.507 | 0.183 | 35.990 | |
## | 0.023 | 0.352 | 0.624 | 0.005 |
## | 0.000 | 0.005 | 0.009 | |
## | 0.000 | 0.002 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Minimalism | 0 | 15 | 12 | 27 |
## | 7.872 | 3.913 | 0.366 | |
## | 0.000 | 0.556 | 0.444 | 0.001 |
## | 0.000 | 0.001 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Modern Jazz | 2 | 9 | 0 | 11 |
## | 0.454 | 7.658 | 4.106 | |
## | 0.182 | 0.818 | 0.000 | 0.000 |
## | 0.000 | 0.001 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Musical Theater | 0 | 0 | 4 | 4 |
## | 1.166 | 1.341 | 4.209 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Musique Concrete | 72 | 45 | 28 | 145 |
## | 20.896 | 0.266 | 12.610 | |
## | 0.497 | 0.310 | 0.193 | 0.004 |
## | 0.006 | 0.003 | 0.002 | |
## | 0.002 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Nerdcore | 0 | 4 | 6 | 10 |
## | 2.916 | 0.125 | 1.377 | |
## | 0.000 | 0.400 | 0.600 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## New Wave | 0 | 14 | 19 | 33 |
## | 9.622 | 0.781 | 3.625 | |
## | 0.000 | 0.424 | 0.576 | 0.001 |
## | 0.000 | 0.001 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## No Wave | 67 | 42 | 31 | 140 |
## | 16.791 | 0.516 | 8.648 | |
## | 0.479 | 0.300 | 0.221 | 0.003 |
## | 0.006 | 0.003 | 0.002 | |
## | 0.002 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Noise | 407 | 308 | 288 | 1003 |
## | 44.873 | 2.359 | 19.936 | |
## | 0.406 | 0.307 | 0.287 | 0.025 |
## | 0.034 | 0.023 | 0.019 | |
## | 0.010 | 0.008 | 0.007 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Noise-Rock | 144 | 99 | 74 | 317 |
## | 28.776 | 0.494 | 16.606 | |
## | 0.454 | 0.312 | 0.233 | 0.008 |
## | 0.012 | 0.007 | 0.005 | |
## | 0.004 | 0.002 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## North African | 0 | 0 | 12 | 12 |
## | 3.499 | 4.022 | 12.627 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Novelty | 1 | 23 | 60 | 84 |
## | 22.533 | 0.943 | 26.169 | |
## | 0.012 | 0.274 | 0.714 | 0.002 |
## | 0.000 | 0.002 | 0.004 | |
## | 0.000 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Nu-Jazz | 0 | 0 | 1 | 1 |
## | 0.292 | 0.335 | 1.052 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Old-Time / Historic | 0 | 87 | 334 | 421 |
## | 122.751 | 20.743 | 199.024 | |
## | 0.000 | 0.207 | 0.793 | 0.010 |
## | 0.000 | 0.006 | 0.022 | |
## | 0.000 | 0.002 | 0.008 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Opera | 0 | 16 | 37 | 53 |
## | 15.453 | 0.175 | 14.982 | |
## | 0.000 | 0.302 | 0.698 | 0.001 |
## | 0.000 | 0.001 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Pacific | 0 | 0 | 7 | 7 |
## | 2.041 | 2.346 | 7.366 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Poetry | 1 | 12 | 5 | 18 |
## | 3.439 | 5.902 | 0.440 | |
## | 0.056 | 0.667 | 0.278 | 0.000 |
## | 0.000 | 0.001 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Polka | 0 | 0 | 21 | 21 |
## | 6.123 | 7.038 | 22.098 | |
## | 0.000 | 0.000 | 1.000 | 0.001 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Pop | 420 | 821 | 657 | 1898 |
## | 32.156 | 53.729 | 3.740 | |
## | 0.221 | 0.433 | 0.346 | 0.047 |
## | 0.035 | 0.060 | 0.043 | |
## | 0.010 | 0.020 | 0.016 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Post-Punk | 145 | 100 | 48 | 293 |
## | 41.538 | 0.033 | 34.436 | |
## | 0.495 | 0.341 | 0.164 | 0.007 |
## | 0.012 | 0.007 | 0.003 | |
## | 0.004 | 0.002 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Post-Rock | 54 | 107 | 86 | 247 |
## | 4.508 | 7.084 | 0.417 | |
## | 0.219 | 0.433 | 0.348 | 0.006 |
## | 0.005 | 0.008 | 0.006 | |
## | 0.001 | 0.003 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Power-Pop | 21 | 95 | 39 | 155 |
## | 12.951 | 35.677 | 6.146 | |
## | 0.135 | 0.613 | 0.252 | 0.004 |
## | 0.002 | 0.007 | 0.003 | |
## | 0.001 | 0.002 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Progressive | 12 | 45 | 36 | 93 |
## | 8.426 | 6.137 | 0.048 | |
## | 0.129 | 0.484 | 0.387 | 0.002 |
## | 0.001 | 0.003 | 0.002 | |
## | 0.000 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Psych-Folk | 70 | 106 | 66 | 242 |
## | 0.004 | 7.640 | 6.554 | |
## | 0.289 | 0.438 | 0.273 | 0.006 |
## | 0.006 | 0.008 | 0.004 | |
## | 0.002 | 0.003 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Psych-Rock | 118 | 156 | 121 | 395 |
## | 0.070 | 4.212 | 4.743 | |
## | 0.299 | 0.395 | 0.306 | 0.010 |
## | 0.010 | 0.011 | 0.008 | |
## | 0.003 | 0.004 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Punk | 737 | 576 | 289 | 1602 |
## | 155.962 | 2.845 | 159.657 | |
## | 0.460 | 0.360 | 0.180 | 0.039 |
## | 0.062 | 0.042 | 0.019 | |
## | 0.018 | 0.014 | 0.007 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Radio | 107 | 64 | 29 | 200 |
## | 40.648 | 0.137 | 27.920 | |
## | 0.535 | 0.320 | 0.145 | 0.005 |
## | 0.009 | 0.005 | 0.002 | |
## | 0.003 | 0.002 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Radio Art | 0 | 14 | 6 | 20 |
## | 5.831 | 7.943 | 0.288 | |
## | 0.000 | 0.700 | 0.300 | 0.000 |
## | 0.000 | 0.001 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Radio Theater | 0 | 3 | 5 | 8 |
## | 2.333 | 0.038 | 1.358 | |
## | 0.000 | 0.375 | 0.625 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Rap | 0 | 4 | 9 | 13 |
## | 3.790 | 0.029 | 3.545 | |
## | 0.000 | 0.308 | 0.692 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Reggae - Dancehall | 0 | 1 | 7 | 8 |
## | 2.333 | 1.054 | 5.395 | |
## | 0.000 | 0.125 | 0.875 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Reggae - Dub | 1 | 19 | 51 | 71 |
## | 18.750 | 0.967 | 22.644 | |
## | 0.014 | 0.268 | 0.718 | 0.002 |
## | 0.000 | 0.001 | 0.003 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Rock | 2209 | 1997 | 1469 | 5675 |
## | 185.715 | 4.744 | 199.041 | |
## | 0.389 | 0.352 | 0.259 | 0.139 |
## | 0.186 | 0.146 | 0.096 | |
## | 0.054 | 0.049 | 0.036 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Rock Opera | 0 | 2 | 17 | 19 |
## | 5.540 | 2.996 | 13.841 | |
## | 0.000 | 0.105 | 0.895 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Rockabilly | 0 | 2 | 12 | 14 |
## | 4.082 | 1.545 | 8.781 | |
## | 0.000 | 0.143 | 0.857 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Shoegaze | 0 | 11 | 25 | 36 |
## | 10.497 | 0.094 | 9.948 | |
## | 0.000 | 0.306 | 0.694 | 0.001 |
## | 0.000 | 0.001 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Singer-Songwriter | 89 | 186 | 249 | 524 |
## | 26.627 | 0.613 | 14.581 | |
## | 0.170 | 0.355 | 0.475 | 0.013 |
## | 0.007 | 0.014 | 0.016 | |
## | 0.002 | 0.005 | 0.006 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Skweee | 0 | 0 | 6 | 6 |
## | 1.749 | 2.011 | 6.314 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Sludge | 12 | 15 | 12 | 39 |
## | 0.035 | 0.285 | 0.449 | |
## | 0.308 | 0.385 | 0.308 | 0.001 |
## | 0.001 | 0.001 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Soul-RnB | 0 | 40 | 136 | 176 |
## | 51.316 | 6.112 | 75.234 | |
## | 0.000 | 0.227 | 0.773 | 0.004 |
## | 0.000 | 0.003 | 0.009 | |
## | 0.000 | 0.001 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Sound Art | 0 | 0 | 4 | 4 |
## | 1.166 | 1.341 | 4.209 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Sound Collage | 66 | 73 | 66 | 205 |
## | 0.649 | 0.268 | 1.447 | |
## | 0.322 | 0.356 | 0.322 | 0.005 |
## | 0.006 | 0.005 | 0.004 | |
## | 0.002 | 0.002 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Sound Effects | 1 | 5 | 27 | 33 |
## | 7.726 | 3.321 | 17.499 | |
## | 0.030 | 0.152 | 0.818 | 0.001 |
## | 0.000 | 0.000 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Sound Poetry | 72 | 19 | 20 | 111 |
## | 48.541 | 8.906 | 11.088 | |
## | 0.649 | 0.171 | 0.180 | 0.003 |
## | 0.006 | 0.001 | 0.001 | |
## | 0.002 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Soundtrack | 36 | 35 | 173 | 244 |
## | 17.360 | 26.758 | 73.684 | |
## | 0.148 | 0.143 | 0.709 | 0.006 |
## | 0.003 | 0.003 | 0.011 | |
## | 0.001 | 0.001 | 0.004 | |
## -------------------------|-----------|-----------|-----------|-----------|
## South Indian Traditional | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Space-Rock | 34 | 54 | 37 | 125 |
## | 0.164 | 3.498 | 2.000 | |
## | 0.272 | 0.432 | 0.296 | 0.003 |
## | 0.003 | 0.004 | 0.002 | |
## | 0.001 | 0.001 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Spanish | 0 | 2 | 31 | 33 |
## | 9.622 | 7.422 | 28.334 | |
## | 0.000 | 0.061 | 0.939 | 0.001 |
## | 0.000 | 0.000 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Spoken | 210 | 114 | 114 | 438 |
## | 53.028 | 7.328 | 14.983 | |
## | 0.479 | 0.260 | 0.260 | 0.011 |
## | 0.018 | 0.008 | 0.007 | |
## | 0.005 | 0.003 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Spoken Weird | 91 | 17 | 21 | 129 |
## | 75.779 | 15.919 | 15.311 | |
## | 0.705 | 0.132 | 0.163 | 0.003 |
## | 0.008 | 0.001 | 0.001 | |
## | 0.002 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Spoken Word | 8 | 2 | 0 | 10 |
## | 8.866 | 0.545 | 3.733 | |
## | 0.800 | 0.200 | 0.000 | 0.000 |
## | 0.001 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Surf | 0 | 0 | 36 | 36 |
## | 10.497 | 12.066 | 37.882 | |
## | 0.000 | 0.000 | 1.000 | 0.001 |
## | 0.000 | 0.000 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Synth Pop | 45 | 122 | 61 | 228 |
## | 6.939 | 27.193 | 6.828 | |
## | 0.197 | 0.535 | 0.268 | 0.006 |
## | 0.004 | 0.009 | 0.004 | |
## | 0.001 | 0.003 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Talk Radio | 0 | 0 | 1 | 1 |
## | 0.292 | 0.335 | 1.052 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Techno | 15 | 122 | 120 | 257 |
## | 47.936 | 14.934 | 6.039 | |
## | 0.058 | 0.475 | 0.467 | 0.006 |
## | 0.001 | 0.009 | 0.008 | |
## | 0.000 | 0.003 | 0.003 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Thrash | 0 | 3 | 2 | 5 |
## | 1.458 | 1.046 | 0.010 | |
## | 0.000 | 0.600 | 0.400 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Trip-Hop | 0 | 12 | 29 | 41 |
## | 11.954 | 0.221 | 12.256 | |
## | 0.000 | 0.293 | 0.707 | 0.001 |
## | 0.000 | 0.001 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Unclassifiable | 130 | 72 | 63 | 265 |
## | 35.991 | 3.184 | 13.042 | |
## | 0.491 | 0.272 | 0.238 | 0.006 |
## | 0.011 | 0.005 | 0.004 | |
## | 0.003 | 0.002 | 0.002 | |
## -------------------------|-----------|-----------|-----------|-----------|
## Column Total | 11894 | 13672 | 15227 | 40793 |
## | 0.292 | 0.335 | 0.373 | |
## -------------------------|-----------|-----------|-----------|-----------|
##
## CrossTable(tracks_disc$language, tracks_disc$listens)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40793
##
##
## | tracks_disc$listens
## tracks_disc$language | low | medium | high | Row Total |
## ---------------------|-----------|-----------|-----------|-----------|
## ar | 0 | 0 | 34 | 34 |
## | 9.913 | 11.395 | 35.777 | |
## | 0.000 | 0.000 | 1.000 | 0.001 |
## | 0.000 | 0.000 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## ---------------------|-----------|-----------|-----------|-----------|
## az | 0 | 0 | 4 | 4 |
## | 1.166 | 1.341 | 4.209 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## bg | 0 | 6 | 8 | 14 |
## | 4.082 | 0.365 | 1.473 | |
## | 0.000 | 0.429 | 0.571 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## bm | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## cs | 4 | 4 | 8 | 16 |
## | 0.095 | 0.346 | 0.688 | |
## | 0.250 | 0.250 | 0.500 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## de | 2 | 57 | 135 | 194 |
## | 52.635 | 0.989 | 54.089 | |
## | 0.010 | 0.294 | 0.696 | 0.005 |
## | 0.000 | 0.004 | 0.009 | |
## | 0.000 | 0.001 | 0.003 | |
## ---------------------|-----------|-----------|-----------|-----------|
## ee | 0 | 0 | 28 | 28 |
## | 8.164 | 9.384 | 29.463 | |
## | 0.000 | 0.000 | 1.000 | 0.001 |
## | 0.000 | 0.000 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## ---------------------|-----------|-----------|-----------|-----------|
## el | 0 | 8 | 7 | 15 |
## | 4.374 | 1.758 | 0.350 | |
## | 0.000 | 0.533 | 0.467 | 0.000 |
## | 0.000 | 0.001 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## en | 11619 | 13246 | 13424 | 38289 |
## | 18.551 | 13.306 | 52.754 | |
## | 0.303 | 0.346 | 0.351 | 0.939 |
## | 0.977 | 0.969 | 0.882 | |
## | 0.285 | 0.325 | 0.329 | |
## ---------------------|-----------|-----------|-----------|-----------|
## es | 109 | 89 | 403 | 601 |
## | 25.034 | 62.753 | 142.285 | |
## | 0.181 | 0.148 | 0.671 | 0.015 |
## | 0.009 | 0.007 | 0.026 | |
## | 0.003 | 0.002 | 0.010 | |
## ---------------------|-----------|-----------|-----------|-----------|
## eu | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## fi | 0 | 9 | 11 | 20 |
## | 5.831 | 0.787 | 1.673 | |
## | 0.000 | 0.450 | 0.550 | 0.000 |
## | 0.000 | 0.001 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## fr | 125 | 99 | 398 | 622 |
## | 17.513 | 57.481 | 118.432 | |
## | 0.201 | 0.159 | 0.640 | 0.015 |
## | 0.011 | 0.007 | 0.026 | |
## | 0.003 | 0.002 | 0.010 | |
## ---------------------|-----------|-----------|-----------|-----------|
## gu | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## ha | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## he | 0 | 50 | 33 | 83 |
## | 24.200 | 17.688 | 0.131 | |
## | 0.000 | 0.602 | 0.398 | 0.002 |
## | 0.000 | 0.004 | 0.002 | |
## | 0.000 | 0.001 | 0.001 | |
## ---------------------|-----------|-----------|-----------|-----------|
## hi | 0 | 0 | 9 | 9 |
## | 2.624 | 3.016 | 9.470 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## hy | 0 | 0 | 4 | 4 |
## | 1.166 | 1.341 | 4.209 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## id | 0 | 0 | 10 | 10 |
## | 2.916 | 3.352 | 10.523 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## it | 6 | 1 | 38 | 45 |
## | 3.864 | 13.148 | 26.763 | |
## | 0.133 | 0.022 | 0.844 | 0.001 |
## | 0.001 | 0.000 | 0.002 | |
## | 0.000 | 0.000 | 0.001 | |
## ---------------------|-----------|-----------|-----------|-----------|
## ja | 1 | 6 | 8 | 15 |
## | 2.602 | 0.188 | 1.029 | |
## | 0.067 | 0.400 | 0.533 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## ka | 0 | 0 | 2 | 2 |
## | 0.583 | 0.670 | 2.105 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## ko | 3 | 0 | 0 | 3 |
## | 5.164 | 1.005 | 1.120 | |
## | 1.000 | 0.000 | 0.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## la | 0 | 0 | 8 | 8 |
## | 2.333 | 2.681 | 8.418 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## lt | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## ms | 0 | 0 | 6 | 6 |
## | 1.749 | 2.011 | 6.314 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## my | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## nl | 8 | 0 | 5 | 13 |
## | 4.675 | 4.357 | 0.004 | |
## | 0.615 | 0.000 | 0.385 | 0.000 |
## | 0.001 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## no | 0 | 0 | 2 | 2 |
## | 0.583 | 0.670 | 2.105 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## pl | 0 | 7 | 67 | 74 |
## | 21.576 | 12.777 | 56.136 | |
## | 0.000 | 0.095 | 0.905 | 0.002 |
## | 0.000 | 0.001 | 0.004 | |
## | 0.000 | 0.000 | 0.002 | |
## ---------------------|-----------|-----------|-----------|-----------|
## pt | 10 | 45 | 294 | 349 |
## | 82.741 | 44.282 | 205.772 | |
## | 0.029 | 0.129 | 0.842 | 0.009 |
## | 0.001 | 0.003 | 0.019 | |
## | 0.000 | 0.001 | 0.007 | |
## ---------------------|-----------|-----------|-----------|-----------|
## ru | 0 | 17 | 108 | 125 |
## | 36.446 | 14.793 | 80.641 | |
## | 0.000 | 0.136 | 0.864 | 0.003 |
## | 0.000 | 0.001 | 0.007 | |
## | 0.000 | 0.000 | 0.003 | |
## ---------------------|-----------|-----------|-----------|-----------|
## sk | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## sr | 7 | 22 | 31 | 60 |
## | 6.295 | 0.178 | 3.305 | |
## | 0.117 | 0.367 | 0.517 | 0.001 |
## | 0.001 | 0.002 | 0.002 | |
## | 0.000 | 0.001 | 0.001 | |
## ---------------------|-----------|-----------|-----------|-----------|
## sw | 0 | 0 | 13 | 13 |
## | 3.790 | 4.357 | 13.679 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.001 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## th | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## tl | 0 | 0 | 2 | 2 |
## | 0.583 | 0.670 | 2.105 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## tr | 0 | 4 | 82 | 86 |
## | 25.075 | 21.378 | 77.561 | |
## | 0.000 | 0.047 | 0.953 | 0.002 |
## | 0.000 | 0.000 | 0.005 | |
## | 0.000 | 0.000 | 0.002 | |
## ---------------------|-----------|-----------|-----------|-----------|
## tw | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## ty | 0 | 0 | 2 | 2 |
## | 0.583 | 0.670 | 2.105 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## uk | 0 | 0 | 3 | 3 |
## | 0.875 | 1.005 | 3.157 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## uz | 0 | 0 | 2 | 2 |
## | 0.583 | 0.670 | 2.105 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## vi | 0 | 0 | 6 | 6 |
## | 1.749 | 2.011 | 6.314 | |
## | 0.000 | 0.000 | 1.000 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## zh | 0 | 2 | 5 | 7 |
## | 2.041 | 0.051 | 2.181 | |
## | 0.000 | 0.286 | 0.714 | 0.000 |
## | 0.000 | 0.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | |
## ---------------------|-----------|-----------|-----------|-----------|
## Column Total | 11894 | 13672 | 15227 | 40793 |
## | 0.292 | 0.335 | 0.373 | |
## ---------------------|-----------|-----------|-----------|-----------|
##
## Genre-Listens Aggregation
aggregate(listens ~ genre, data = tracks_disc, FUN = length)## genre listens
## 1 20th Century Classical 60
## 2 African 57
## 3 Afrobeat 5
## 4 Alternative Hip-Hop 120
## 5 Ambient 286
## 6 Ambient Electronic 377
## 7 Americana 151
## 8 Asia-Far East 29
## 9 Audio Collage 287
## 10 Avant-Garde 1136
## 11 Balkan 83
## 12 Banter 4
## 13 Big Band/Swing 19
## 14 Bigbeat 11
## 15 Black-Metal 20
## 16 Bluegrass 87
## 17 Blues 162
## 18 Brazilian 75
## 19 Breakbeat 7
## 20 Breakcore - Hard 77
## 21 British Folk 18
## 22 Celtic 15
## 23 Chill-out 11
## 24 Chip Music 267
## 25 Chiptune 100
## 26 Choral Music 46
## 27 Christmas 4
## 28 Classical 364
## 29 Comedy 55
## 30 Compilation 85
## 31 Composed Music 79
## 32 Contemporary Classical 4
## 33 Country 284
## 34 Cumbia 1
## 35 Dance 246
## 36 Death-Metal 5
## 37 Deep Funk 1
## 38 Disco 98
## 39 Downtempo 2
## 40 Drone 167
## 41 Drum & Bass 3
## 42 Dubstep 71
## 43 Easy Listening 59
## 44 Easy Listening: Vocal 4
## 45 Electro-Punk 142
## 46 Electroacoustic 316
## 47 Electronic 3385
## 48 Europe 113
## 49 Experimental 4330
## 50 Experimental Pop 1074
## 51 Fado 1
## 52 Field Recordings 271
## 53 Flamenco 1
## 54 Folk 1983
## 55 Freak-Folk 52
## 56 Free-Folk 92
## 57 Free-Jazz 250
## 58 French 11
## 59 Funk 44
## 60 Garage 476
## 61 Glitch 108
## 62 Goth 22
## 63 Grindcore 10
## 64 Hardcore 162
## 65 Hip-Hop 889
## 66 Hip-Hop Beats 3
## 67 Holiday 33
## 68 House 77
## 69 IDM 33
## 70 Improv 465
## 71 Indian 17
## 72 Indie-Rock 884
## 73 Industrial 113
## 74 Instrumental 548
## 75 International 695
## 76 Interview 40
## 77 Jazz 615
## 78 Jazz: Out 96
## 79 Jazz: Vocal 49
## 80 Kid-Friendly 15
## 81 Krautrock 134
## 82 Latin 13
## 83 Latin America 79
## 84 Lo-Fi 949
## 85 Loud-Rock 486
## 86 Lounge 53
## 87 Metal 155
## 88 Middle East 45
## 89 Minimal Electronic 213
## 90 Minimalism 27
## 91 Modern Jazz 11
## 92 Musical Theater 4
## 93 Musique Concrete 145
## 94 Nerdcore 10
## 95 New Wave 33
## 96 No Wave 140
## 97 Noise 1003
## 98 Noise-Rock 317
## 99 North African 12
## 100 Novelty 84
## 101 Nu-Jazz 1
## 102 Old-Time / Historic 421
## 103 Opera 53
## 104 Pacific 7
## 105 Poetry 18
## 106 Polka 21
## 107 Pop 1898
## 108 Post-Punk 293
## 109 Post-Rock 247
## 110 Power-Pop 155
## 111 Progressive 93
## 112 Psych-Folk 242
## 113 Psych-Rock 395
## 114 Punk 1602
## 115 Radio 200
## 116 Radio Art 20
## 117 Radio Theater 8
## 118 Rap 13
## 119 Reggae - Dancehall 8
## 120 Reggae - Dub 71
## 121 Rock 5675
## 122 Rock Opera 19
## 123 Rockabilly 14
## 124 Shoegaze 36
## 125 Singer-Songwriter 524
## 126 Skweee 6
## 127 Sludge 39
## 128 Soul-RnB 176
## 129 Sound Art 4
## 130 Sound Collage 205
## 131 Sound Effects 33
## 132 Sound Poetry 111
## 133 Soundtrack 244
## 134 South Indian Traditional 3
## 135 Space-Rock 125
## 136 Spanish 33
## 137 Spoken 438
## 138 Spoken Weird 129
## 139 Spoken Word 10
## 140 Surf 36
## 141 Synth Pop 228
## 142 Talk Radio 1
## 143 Techno 257
## 144 Thrash 5
## 145 Trip-Hop 41
## 146 Unclassifiable 265Different genres have vastly different representation in the dataset, with some genres appearing thousands of times while others appear only dozens of times.
Association Rules Mining
Now before starting the algorithms, we need to check what we have built.
summary(tracks_basket)## transactions as itemMatrix in sparse format with
## 40793 rows (elements/itemsets/transactions) and
## 193 columns (items) and a density of 0.01554404
##
## most frequent items:
## language=en listens=high listens=medium listens=low genre=Rock
## 38289 15227 13672 11894 5675
## (Other)
## 37622
##
## element (itemset/transaction) length distribution:
## sizes
## 3
## 40793
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3 3 3 3 3 3
##
## includes extended item information - examples:
## labels variables levels
## 1 language=ar language ar
## 2 language=az language az
## 3 language=bg language bg
##
## includes extended transaction information - examples:
## transactionID
## 1 1
## 2 2
## 3 3The dataset consists of 40,793 transactions with 193 items and a low density (1.5%), which is typical for market basket data. The most frequent items correspond to language (english language domination) and listening-level categories (equally splitted in three earlier), confirming the consistency of the constructed transactions.
Approach 1: Initial Exploration
Initial parameters with large support and confidence to get a manageable number of rules.
rules_1 <- apriori(
tracks_basket,
parameter = list(
support = 0.01,
confidence = 0.5
)
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.5 0.1 1 none FALSE TRUE 5 0.01 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 407
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[193 item(s), 40793 transaction(s)] done [0.00s].
## sorting and recoding items ... [27 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [43 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules_1 <- rules_1[!is.redundant(rules_1)]rules_1_by_lift <- sort(rules_1, by = "lift", decreasing = TRUE)
inspect(head(rules_1_by_lift, n = 10))## lhs rhs support confidence coverage lift count
## [1] {genre=International} => {listens=high} 0.01409556 0.8273381 0.01703724 2.216432 575
## [2] {genre=Improv} => {language=en} 0.01139901 1.0000000 0.01139901 1.065397 465
## [3] {listens=low,
## genre=Folk} => {language=en} 0.01088422 1.0000000 0.01088422 1.065397 444
## [4] {listens=low,
## genre=Avant-Garde} => {language=en} 0.01262472 0.9980620 0.01264923 1.063333 515
## [5] {listens=low,
## genre=Experimental} => {language=en} 0.04471355 0.9923830 0.04505675 1.057282 1824
## [6] {genre=Spoken} => {language=en} 0.01063908 0.9908676 0.01073714 1.055668 434
## [7] {listens=medium,
## genre=Electronic} => {language=en} 0.03135342 0.9861218 0.03179467 1.050612 1279
## [8] {genre=Loud-Rock} => {language=en} 0.01174221 0.9855967 0.01191381 1.050052 479
## [9] {listens=low,
## genre=Electronic} => {language=en} 0.01958669 0.9852035 0.01988086 1.049633 799
## [10] {listens=medium,
## genre=Experimental Pop} => {language=en} 0.01115387 0.9827214 0.01134999 1.046989 455plot(
head(rules_1, n = 20),
method = "graph",
main = "Association Rules Network (Approach 1)"
)## Available control parameters (with default values):
## layout = stress
## circular = FALSE
## ggraphdots = NULL
## edges = <environment>
## nodes = <environment>
## nodetext = <environment>
## colors = c("#EE0000FF", "#EEEEEEFF")
## engine = ggplot2
## max = 100
## verbose = FALSE
Using relatively loose thresholds for support and confidence allows us to generate an initial set of association rules and explore the global structure of the data. The strongest rules mostly involve associations between genre or listening-level categories and the English language as the main item in the center. These rules typically show high confidence but relatively low lift values, suggesting that while the associations are frequent, they are partly explained by the dominance of English-language tracks in the dataset. The network visualization highlights the central role of the English language item, which acts as a hub connected to many genres and popularity levels
Approach 2: Stricter Parameters
Second try with more strict parameters to focus on stronger associations.
rules_2 <- apriori(
tracks_basket,
parameter = list(
support = 0.05,
confidence = 0.6
)
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.6 0.1 1 none FALSE TRUE 5 0.05 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 2039
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[193 item(s), 40793 transaction(s)] done [0.00s].
## sorting and recoding items ... [7 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [8 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules_2 <- rules_2[!is.redundant(rules_2)]rules_2_by_lift <- sort(rules_2, by = "lift", decreasing = TRUE)
inspect(head(rules_2_by_lift, n = 10))## lhs rhs support confidence coverage
## [1] {genre=Electronic} => {language=en} 0.08109234 0.9772526 0.08297992
## [2] {listens=low} => {language=en} 0.28482828 0.9768791 0.29156963
## [3] {listens=medium} => {language=en} 0.32471257 0.9688414 0.33515554
## [4] {genre=Experimental} => {language=en} 0.10244405 0.9651270 0.10614566
## [5] {genre=Rock} => {language=en} 0.13271885 0.9540088 0.13911701
## [6] {} => {language=en} 0.93861692 0.9386169 1.00000000
## lift count
## [1] 1.041162 3308
## [2] 1.040764 11619
## [3] 1.032201 13246
## [4] 1.028244 4179
## [5] 1.016398 5414
## [6] 1.000000 38289plot(
head(rules_2, n = 20),
method = "graph",
main = "Association Rules Network (Approach 2)"
)## Available control parameters (with default values):
## layout = stress
## circular = FALSE
## ggraphdots = NULL
## edges = <environment>
## nodes = <environment>
## nodetext = <environment>
## colors = c("#EE0000FF", "#EEEEEEFF")
## engine = ggplot2
## max = 100
## verbose = FALSE
By increasing the minimum support and confidence thresholds, the number of extracted rules decreases significantly. The resulting network is simpler and easier to read, with fewer nodes and connections. This filtering step allows us to focus on more robust and reliable associations. Compared to the first approach, it also exhibit slightly higher lift values and are less redundant. However, many of the remainiing rules still involve the English language, confirming its strong influence across the dataset.
Approach 3: Focus on Strong Multi-Item Rules
Third try with a focus on strong rules involving multiple items.
rules_3 <- apriori(
tracks_basket,
parameter = list(
support = 0.03,
confidence = 0.7,
minlen = 2,
maxlen = 4
)
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.03 2
## maxlen target ext
## 4 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 1223
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[193 item(s), 40793 transaction(s)] done [0.00s].
## sorting and recoding items ... [10 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [16 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules_3 <- rules_3[!is.redundant(rules_3)]summary(rules_3)## set of 13 rules
##
## rule length distribution (lhs + rhs):sizes
## 2 3
## 9 4
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 2.000 2.000 2.308 3.000 3.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.03135 Min. :0.8816 Min. :0.03179 Min. :0.9392
## 1st Qu.:0.04464 1st Qu.:0.9594 1st Qu.:0.04506 1st Qu.:1.0222
## Median :0.04773 Median :0.9688 Median :0.04895 Median :1.0322
## Mean :0.11834 Mean :0.9630 Mean :0.12467 Mean :1.0260
## 3rd Qu.:0.13272 3rd Qu.:0.9772 3rd Qu.:0.13912 3rd Qu.:1.0411
## Max. :0.32908 Max. :0.9924 Max. :0.37327 Max. :1.0573
## count
## Min. : 1279
## 1st Qu.: 1821
## Median : 1947
## Mean : 4828
## 3rd Qu.: 5414
## Max. :13424
##
## mining info:
## data ntransactions support confidence
## tracks_basket 40793 0.03 0.7
## call
## apriori(data = tracks_basket, parameter = list(support = 0.03, confidence = 0.7, minlen = 2, maxlen = 4))inspect(head(sort(rules_3, by = "lift"), n = 10))## lhs rhs support
## [1] {listens=low, genre=Experimental} => {language=en} 0.04471355
## [2] {listens=medium, genre=Electronic} => {language=en} 0.03135342
## [3] {genre=Electronic} => {language=en} 0.08109234
## [4] {listens=medium, genre=Experimental} => {language=en} 0.03152502
## [5] {listens=low} => {language=en} 0.28482828
## [6] {listens=medium, genre=Rock} => {language=en} 0.04772878
## [7] {listens=medium} => {language=en} 0.32471257
## [8] {genre=Experimental} => {language=en} 0.10244405
## [9] {genre=Punk} => {language=en} 0.03777609
## [10] {genre=Pop} => {language=en} 0.04464001
## confidence coverage lift count
## [1] 0.9923830 0.04505675 1.057282 1824
## [2] 0.9861218 0.03179467 1.050612 1279
## [3] 0.9772526 0.08297992 1.041162 3308
## [4] 0.9772036 0.03226044 1.041110 1286
## [5] 0.9768791 0.29156963 1.040764 11619
## [6] 0.9749624 0.04895448 1.038722 1947
## [7] 0.9688414 0.33515554 1.032201 13246
## [8] 0.9651270 0.10614566 1.028244 4179
## [9] 0.9619226 0.03927144 1.024830 1541
## [10] 0.9594310 0.04652759 1.022175 1821plot(
head(rules_3, n = 20),
method = "graph",
main = "Association Rules Network (Approach 3)"
)## Available control parameters (with default values):
## layout = stress
## circular = FALSE
## ggraphdots = NULL
## edges = <environment>
## nodes = <environment>
## nodetext = <environment>
## colors = c("#EE0000FF", "#EEEEEEFF")
## engine = ggplot2
## max = 100
## verbose = FALSE
In this third approach, we explicitly focus on multi-item rules by
increasing the minimum rule length. This allows us to identify more
specific associations involving combinations of genres and listening
levels.
The network representation shows a more fragmented structure, with fewer but more specific associations. This confirms that increasing rule complexity leads to more targeted patterns, at the cost of reduced generality.
ECLAT Comparison
ECLAT is an alternative algorithm for finding frequent itemsets. Let’s compare with Apriori.
itemsets_eclat <- eclat(
tracks_basket,
parameter = list(
support = 0.01,
minlen = 2,
maxlen = 4
)
)## Eclat
##
## parameter specification:
## tidLists support minlen maxlen target ext
## FALSE 0.01 2 4 frequent itemsets TRUE
##
## algorithmic control:
## sparse sort verbose
## 7 -2 TRUE
##
## Absolute minimum support count: 407
##
## create itemset ...
## set transactions ...[193 item(s), 40793 transaction(s)] done [0.00s].
## sorting and recoding items ... [27 item(s)] done [0.00s].
## creating sparse bit matrix ... [27 row(s), 40793 column(s)] done [0.00s].
## writing ... [62 set(s)] done [0.00s].
## Creating S4 object ... done [0.00s].summary(itemsets_eclat)## set of 62 itemsets
##
## most frequent items:
## language=en listens=low listens=medium listens=high
## 41 16 15 12
## genre=Electronic (Other)
## 7 52
##
## element (itemset/transaction) length distribution:sizes
## 2 3
## 43 19
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 2.000 2.000 2.306 3.000 3.000
##
## summary of quality measures:
## support count
## Min. :0.01000 Min. : 408.0
## 1st Qu.:0.01382 1st Qu.: 563.8
## Median :0.02022 Median : 825.0
## Mean :0.04145 Mean : 1690.9
## 3rd Qu.:0.03517 3rd Qu.: 1434.5
## Max. :0.32908 Max. :13424.0
##
## includes transaction ID lists: FALSE
##
## mining info:
## data ntransactions support
## tracks_basket 40793 0.01
## call
## eclat(data = tracks_basket, parameter = list(support = 0.01, minlen = 2, maxlen = 4))rules_eclat <- ruleInduction(
itemsets_eclat,
transactions = tracks_basket,
confidence = 0.5
)
inspect(head(sort(rules_eclat, by = "lift"), n = 10))## lhs rhs support confidence lift itemset
## [1] {genre=International} => {listens=high} 0.01409556 0.8273381 2.216432 8
## [2] {genre=Improv} => {language=en} 0.01139901 1.0000000 1.065397 2
## [3] {listens=low,
## genre=Folk} => {language=en} 0.01088422 1.0000000 1.065397 32
## [4] {listens=low,
## genre=Avant-Garde} => {language=en} 0.01262472 0.9980620 1.063333 17
## [5] {listens=low,
## genre=Experimental} => {language=en} 0.04471355 0.9923830 1.057282 46
## [6] {genre=Spoken} => {language=en} 0.01063908 0.9908676 1.055668 1
## [7] {listens=medium,
## genre=Electronic} => {language=en} 0.03135342 0.9861218 1.050612 40
## [8] {genre=Loud-Rock} => {language=en} 0.01174221 0.9855967 1.050052 4
## [9] {listens=low,
## genre=Electronic} => {language=en} 0.01958669 0.9852035 1.049633 39
## [10] {listens=medium,
## genre=Experimental Pop} => {language=en} 0.01115387 0.9827214 1.046989 14ECLAT was used as an alternative algorithm to Apriori in order to compare the resulting rule sets. While ECLAT produces a higher number of rules under similar parameters, the average lift values remain lower than the best try obtained with Apriori.
This suggests that both algorithms capture similar underlying patterns in the data, with differences mainly related to computational strategy rather than rule quality.
Algorithm Comparison
comparison <- data.frame(
Algorithm = c("Apriori_1", "Apriori_2", "Apriori_3", "ECLAT"),
Support = c(0.01, 0.05, 0.03, 0.01),
Confidence = c(0.5, 0.6, 0.7, 0.5),
N_Rules = c(
length(rules_1),
length(rules_2),
length(rules_3),
length(rules_eclat)
),
Avg_Lift = c(
mean(quality(rules_1)$lift),
mean(quality(rules_2)$lift),
mean(quality(rules_3)$lift),
mean(quality(rules_eclat)$lift)
)
)
print(comparison)## Algorithm Support Confidence N_Rules Avg_Lift
## 1 Apriori_1 0.01 0.5 31 1.074269
## 2 Apriori_2 0.05 0.6 6 1.026462
## 3 Apriori_3 0.03 0.7 13 1.025998
## 4 ECLAT 0.01 0.5 42 1.049209- Apriori_1 (lenient): Generates the highest average lift, and with the second highest amount of rules.
- Apriori_2 (strict): Fewer rules and lower quality metrics
- Apriori_3 (focused): Also low amount of rules and low quality
- ECLAT: Comparable to Apriori_1 but with higher amount of rules, lower average lift though, but better computational efficiency
Grouped Visualization for the first apriori approach
plot(
rules_1,
method = "grouped",
main = "Grouped Association Rules"
)## Available control parameters (with default values):
## k = 20
## aggr.fun = function (x, ...) UseMethod("mean")
## rhs_max = 10
## lhs_label_items = 2
## col = c("#EE0000FF", "#EEEEEEFF")
## groups = NULL
## engine = ggplot2
## verbose = FALSE
The grouped visualization helps summarize the extracted rules by clustering similar left-hand-side itemsets. This representation facilitates the identification of recurring patterns involving genres, language, and popularity levels, although the interpretability remains limited by the dominance of certain items, especially the english language.
Conclusion
This association rules analysis highlights recurring co-occurrence patterns between genre, language, and popularity categories. While many rules are influenced by the dominant presence of English-language tracks, varying the algorithm parameters and rule complexity reveals patterns at different levels of granularity.
The discretization strategy enabled interpretable results, but the analysis also illustrates the limitations of association rules when applied to imbalanced datasets. Future work could explore alternative discretization schemes or hierarchical genre representations to extract more nuanced associations.