library(DBI)
library(plyr)
library(arulesViz)
## Loading required package: arules
## Loading required package: Matrix
##
## Attaching package: 'arules'
## The following objects are masked from 'package:base':
##
## abbreviate, write
library(DBI)
library(dbplyr)
library(odbc)
require(RJDBC)
## Loading required package: RJDBC
## Loading required package: rJava
require(RODBC)
## Loading required package: RODBC
library(arules)
library(arulesViz)
bread_basket <- read.csv("F:/Market Basket/Market_Basket/bread basket.csv")
View(bread_basket)
bread_basket=as.data.frame(bread_basket)
head(bread_basket,10)
## Transaction Item date_time period_day weekday_weekend
## 1 1 Bread 30-10-2016 09:58 morning weekend
## 2 2 Scandinavian 30-10-2016 10:05 morning weekend
## 3 2 Scandinavian 30-10-2016 10:05 morning weekend
## 4 3 Hot chocolate 30-10-2016 10:07 morning weekend
## 5 3 Jam 30-10-2016 10:07 morning weekend
## 6 3 Cookies 30-10-2016 10:07 morning weekend
## 7 4 Muffin 30-10-2016 10:08 morning weekend
## 8 5 Coffee 30-10-2016 10:13 morning weekend
## 9 5 Pastry 30-10-2016 10:13 morning weekend
## 10 5 Bread 30-10-2016 10:13 morning weekend
sum(is.na(bread_basket))
## [1] 0
sorted <- bread_basket[order(bread_basket$Transaction),]
sorted$Transaction <- as.numeric(sorted$Transaction)
str(sorted)
## 'data.frame': 20507 obs. of 5 variables:
## $ Transaction : num 1 2 2 3 3 3 4 5 5 5 ...
## $ Item : chr "Bread" "Scandinavian" "Scandinavian" "Hot chocolate" ...
## $ date_time : chr "30-10-2016 09:58" "30-10-2016 10:05" "30-10-2016 10:05" "30-10-2016 10:07" ...
## $ period_day : chr "morning" "morning" "morning" "morning" ...
## $ weekday_weekend: chr "weekend" "weekend" "weekend" "weekend" ...
itemList <- ddply(sorted, c("Transaction","date_time"),
function(df1)
paste(df1$Item, collapse = ","))
itemList$Transaction <- NULL
itemList$date_time <- NULL
itemList$period_day <- NULL
itemList$weekday_weekend <- NULL
colnames (itemList) <- c("itemList")
write.csv(itemList, "itemList.csv", quote=FALSE, row.names=TRUE)
head(itemList, 10)
## itemList
## 1 Bread
## 2 Scandinavian,Scandinavian
## 3 Hot chocolate,Jam,Cookies
## 4 Muffin
## 5 Coffee,Pastry,Bread
## 6 Medialuna,Pastry,Muffin
## 7 Medialuna,Pastry,Coffee,Tea
## 8 Pastry,Bread
## 9 Bread,Muffin
## 10 Scandinavian,Medialuna
str(itemList)
## 'data.frame': 9465 obs. of 1 variable:
## $ itemList: chr "Bread" "Scandinavian,Scandinavian" "Hot chocolate,Jam,Cookies" "Muffin" ...
transaksi = read.transactions("./itemList.csv", format="basket",sep=",",cols=1)
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in asMethod(object): removing duplicated items in transactions
transaksi
## transactions in sparse format with
## 9466 transactions (rows) and
## 105 items (columns)
basket_rules = apriori(transaksi, parameter = list(sup=0.01, minlen=3, conf=0.1, target="rules"))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.1 0.1 1 none FALSE TRUE 5 0.01 3
## 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: 94
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[105 item(s), 9466 transaction(s)] done [0.00s].
## sorting and recoding items ... [30 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [9 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
print(length(basket_rules))
## [1] 9
summary(basket_rules)
## set of 9 rules
##
## rule length distribution (lhs + rhs):sizes
## 3
## 9
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3 3 3 3 3 3
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01004 Min. :0.1115 Min. :0.02335 Min. :0.5611
## 1st Qu.:0.01004 1st Qu.:0.1834 1st Qu.:0.02916 1st Qu.:0.8032
## Median :0.01004 Median :0.2013 Median :0.04986 Median :0.8990
## Mean :0.01042 Mean :0.2529 Mean :0.05145 Mean :1.0683
## 3rd Qu.:0.01120 3rd Qu.:0.3841 3rd Qu.:0.05472 3rd Qu.:1.2869
## Max. :0.01120 Max. :0.4299 Max. :0.09001 Max. :1.9382
## count
## Min. : 95.00
## 1st Qu.: 95.00
## Median : 95.00
## Mean : 98.67
## 3rd Qu.:106.00
## Max. :106.00
##
## mining info:
## data ntransactions support confidence
## transaksi 9466 0.01 0.1
## call
## apriori(data = transaksi, parameter = list(sup = 0.01, minlen = 3, conf = 0.1, target = "rules"))
inspect(basket_rules)
## lhs rhs support confidence coverage lift
## [1] {Bread, Pastry} => {Coffee} 0.01119797 0.3840580 0.02915698 0.8032463
## [2] {Coffee, Pastry} => {Bread} 0.01119797 0.2360802 0.04743292 0.7222802
## [3] {Bread, Coffee} => {Pastry} 0.01119797 0.1244131 0.09000634 1.4467996
## [4] {Cake, Tea} => {Coffee} 0.01003592 0.4222222 0.02376928 0.8830657
## [5] {Cake, Coffee} => {Tea} 0.01003592 0.1833977 0.05472216 1.2869107
## [6] {Coffee, Tea} => {Cake} 0.01003592 0.2012712 0.04986267 1.9381821
## [7] {Bread, Cake} => {Coffee} 0.01003592 0.4298643 0.02334671 0.8990488
## [8] {Cake, Coffee} => {Bread} 0.01003592 0.1833977 0.05472216 0.5610997
## [9] {Bread, Coffee} => {Cake} 0.01003592 0.1115023 0.09000634 1.0737347
## count
## [1] 106
## [2] 106
## [3] 106
## [4] 95
## [5] 95
## [6] 95
## [7] 95
## [8] 95
## [9] 95
plot(basket_rules, jitter=0)
itemFrequencyPlot(transaksi, topN=10)
plot(basket_rules, method="graph")
plot(basket_rules, method="paracoord")
