Sintaks Modul AR APRIORI & FP GROWTH
aldi8402
2025-04-16
AR APRIORI
##### AP APRIORI
library(arules)## Warning: package 'arules' was built under R version 4.4.3## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(arulesViz)## Warning: package 'arulesViz' was built under R version 4.4.3library(RColorBrewer)
data("Groceries")
summary(Groceries)## transactions as itemMatrix in sparse format with
## 9835 rows (elements/itemsets/transactions) and
## 169 columns (items) and a density of 0.02609146
##
## most frequent items:
## whole milk other vegetables rolls/buns soda
## 2513 1903 1809 1715
## yogurt (Other)
## 1372 34055
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 2159 1643 1299 1005 855 645 545 438 350 246 182 117 78 77 55 46
## 17 18 19 20 21 22 23 24 26 27 28 29 32
## 29 14 14 9 11 4 6 1 1 1 1 3 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 4.409 6.000 32.000
##
## includes extended item information - examples:
## labels level2 level1
## 1 frankfurter sausage meat and sausage
## 2 sausage sausage meat and sausage
## 3 liver loaf sausage meat and sausageinspect(Groceries[1:5])## items
## [1] {citrus fruit,
## semi-finished bread,
## margarine,
## ready soups}
## [2] {tropical fruit,
## yogurt,
## coffee}
## [3] {whole milk}
## [4] {pip fruit,
## yogurt,
## cream cheese ,
## meat spreads}
## [5] {other vegetables,
## whole milk,
## condensed milk,
## long life bakery product}#Parameter supp = 0.01 artinya itemset harus muncul di minimal 1% transaksi.
#Parameter conf = 0.2 artinya aturan yang diambil memiliki kepercayaan minimal 20%.
rules <- apriori(Groceries, parameter = list(supp = 0.01, conf = 0.2))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.2 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: 98
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
## sorting and recoding items ... [88 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [232 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules## set of 232 rulesinspect(rules[1:10])## lhs rhs support confidence coverage
## [1] {} => {whole milk} 0.25551601 0.2555160 1.00000000
## [2] {hard cheese} => {whole milk} 0.01006609 0.4107884 0.02450432
## [3] {butter milk} => {other vegetables} 0.01037112 0.3709091 0.02796136
## [4] {butter milk} => {whole milk} 0.01159126 0.4145455 0.02796136
## [5] {ham} => {whole milk} 0.01148958 0.4414062 0.02602949
## [6] {sliced cheese} => {whole milk} 0.01077783 0.4398340 0.02450432
## [7] {oil} => {whole milk} 0.01128622 0.4021739 0.02806304
## [8] {onions} => {other vegetables} 0.01423488 0.4590164 0.03101169
## [9] {onions} => {whole milk} 0.01209964 0.3901639 0.03101169
## [10] {berries} => {yogurt} 0.01057448 0.3180428 0.03324860
## lift count
## [1] 1.000000 2513
## [2] 1.607682 99
## [3] 1.916916 102
## [4] 1.622385 114
## [5] 1.727509 113
## [6] 1.721356 106
## [7] 1.573968 111
## [8] 2.372268 140
## [9] 1.526965 119
## [10] 2.279848 104arules::itemFrequencyPlot(Groceries, topN = 20,
col = brewer.pal(8, 'Pastel2'),
main = 'Relative Item Frequency Plot',
type = "relative",
ylab = "Item Frequency (Relative)")
Grafik Relative Item Frequency Plot menunjukkan bahwa whole milk merupakan item yang paling sering muncul dalam transaksi, dengan frekuensi relatif sekitar 25%, diikuti oleh other vegetables dan rolls/buns dengan frekuensi masing-masing sekitar 23% dan 19%. Item seperti soda, yogurt, dan bottled water juga termasuk dalam daftar produk yang cukup sering dibeli, dengan frekuensi berkisar antara 15% hingga 18%. Sementara itu, item seperti domestic eggs, brown bread, dan whipped/sour cream menempati posisi terbawah dari 20 item teratas, dengan frekuensi relatif sekitar 7%. Pola ini menunjukkan bahwa produk-produk dengan frekuensi tinggi, terutama whole milk dan vegetables, sangat potensial untuk dianalisis lebih lanjut dalam pembentukan aturan asosiasi serta dapat ditempatkan secara strategis untuk meningkatkan pembelian bersamaan di toko.
AR FP-GROWTH
##### AP FP-Growt
# Load data
library(arules)
data(Groceries)
class(Groceries)## [1] "transactions"
## attr(,"package")
## [1] "arules"summary(Groceries)## transactions as itemMatrix in sparse format with
## 9835 rows (elements/itemsets/transactions) and
## 169 columns (items) and a density of 0.02609146
##
## most frequent items:
## whole milk other vegetables rolls/buns soda
## 2513 1903 1809 1715
## yogurt (Other)
## 1372 34055
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 2159 1643 1299 1005 855 645 545 438 350 246 182 117 78 77 55 46
## 17 18 19 20 21 22 23 24 26 27 28 29 32
## 29 14 14 9 11 4 6 1 1 1 1 3 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 4.409 6.000 32.000
##
## includes extended item information - examples:
## labels level2 level1
## 1 frankfurter sausage meat and sausage
## 2 sausage sausage meat and sausage
## 3 liver loaf sausage meat and sausageinspect(head(Groceries, 3))## items
## [1] {citrus fruit,
## semi-finished bread,
## margarine,
## ready soups}
## [2] {tropical fruit,
## yogurt,
## coffee}
## [3] {whole milk}frequentItems <- eclat (Groceries, parameter = list(supp = 0.07, maxlen = 15)) # calculates support for frequent items## Eclat
##
## parameter specification:
## tidLists support minlen maxlen target ext
## FALSE 0.07 1 15 frequent itemsets TRUE
##
## algorithmic control:
## sparse sort verbose
## 7 -2 TRUE
##
## Absolute minimum support count: 688
##
## create itemset ...
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
## sorting and recoding items ... [18 item(s)] done [0.00s].
## creating sparse bit matrix ... [18 row(s), 9835 column(s)] done [0.00s].
## writing ... [19 set(s)] done [0.00s].
## Creating S4 object ... done [0.00s].inspect(frequentItems)## items support count
## [1] {other vegetables, whole milk} 0.07483477 736
## [2] {whole milk} 0.25551601 2513
## [3] {other vegetables} 0.19349263 1903
## [4] {rolls/buns} 0.18393493 1809
## [5] {yogurt} 0.13950178 1372
## [6] {soda} 0.17437722 1715
## [7] {root vegetables} 0.10899847 1072
## [8] {tropical fruit} 0.10493137 1032
## [9] {bottled water} 0.11052364 1087
## [10] {sausage} 0.09395018 924
## [11] {shopping bags} 0.09852567 969
## [12] {citrus fruit} 0.08276563 814
## [13] {pastry} 0.08896797 875
## [14] {pip fruit} 0.07564820 744
## [15] {whipped/sour cream} 0.07168277 705
## [16] {fruit/vegetable juice} 0.07229283 711
## [17] {newspapers} 0.07981698 785
## [18] {bottled beer} 0.08052872 792
## [19] {canned beer} 0.07768175 764itemFrequencyPlot(Groceries, topN=10, type="absolute", main="Item Frequency") # plot frequent items
rules <- apriori (Groceries, parameter = list(supp = 0.001, conf = 0.5)) # Min Support as 0.001, confidence as 0.5.## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.5 0.1 1 none FALSE TRUE 5 0.001 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: 9
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
## sorting and recoding items ... [157 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 5 6 done [0.02s].
## writing ... [5668 rule(s)] done [0.00s].
## creating S4 object ... done [0.01s].rules_conf <- sort (rules, by="confidence", decreasing=TRUE) # 'high-confidence' rules.
inspect(head(rules_conf)) # show the support, lift and confidence for all rules## lhs rhs support confidence coverage lift count
## [1] {rice,
## sugar} => {whole milk} 0.001220132 1 0.001220132 3.913649 12
## [2] {canned fish,
## hygiene articles} => {whole milk} 0.001118454 1 0.001118454 3.913649 11
## [3] {root vegetables,
## butter,
## rice} => {whole milk} 0.001016777 1 0.001016777 3.913649 10
## [4] {root vegetables,
## whipped/sour cream,
## flour} => {whole milk} 0.001728521 1 0.001728521 3.913649 17
## [5] {butter,
## soft cheese,
## domestic eggs} => {whole milk} 0.001016777 1 0.001016777 3.913649 10
## [6] {citrus fruit,
## root vegetables,
## soft cheese} => {other vegetables} 0.001016777 1 0.001016777 5.168156 10rules <- apriori(Groceries, parameter = list (supp = 0.001, conf = 0.2, maxlen=3)) # maxlen = 3 limits the elements in a rule to 3## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.2 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 3 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 9
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
## sorting and recoding items ... [157 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3## Warning in apriori(Groceries, parameter = list(supp = 0.001, conf = 0.2, :
## Mining stopped (maxlen reached). Only patterns up to a length of 3 returned!## done [0.01s].
## writing ... [9958 rule(s)] done [0.00s].
## creating S4 object ... done [0.01s].#summary of rules
summary(rules)## set of 9958 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3
## 1 620 9337
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 3.000 2.938 3.000 3.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.001017 Min. :0.2000 Min. :0.001118 Min. : 0.8028
## 1st Qu.:0.001220 1st Qu.:0.2439 1st Qu.:0.003762 1st Qu.: 1.8658
## Median :0.001627 Median :0.3077 Median :0.005287 Median : 2.3603
## Mean :0.002554 Mean :0.3452 Mean :0.008272 Mean : 2.6338
## 3rd Qu.:0.002542 3rd Qu.:0.4194 3rd Qu.:0.008236 3rd Qu.: 3.0742
## Max. :0.255516 Max. :1.0000 Max. :1.000000 Max. :35.7158
## count
## Min. : 10.00
## 1st Qu.: 12.00
## Median : 16.00
## Mean : 25.12
## 3rd Qu.: 25.00
## Max. :2513.00
##
## mining info:
## data ntransactions support confidence
## Groceries 9835 0.001 0.2
## call
## apriori(data = Groceries, parameter = list(supp = 0.001, conf = 0.2, maxlen = 3))# Inspect rules
#inspect(rules)
#inspect top 5 rules by highest lift
inspect(head(sort(rules, by ="lift"),5))## lhs rhs support
## [1] {bottled beer, red/blush wine} => {liquor} 0.001931876
## [2] {hamburger meat, soda} => {Instant food products} 0.001220132
## [3] {ham, white bread} => {processed cheese} 0.001931876
## [4] {bottled beer, liquor} => {red/blush wine} 0.001931876
## [5] {Instant food products, soda} => {hamburger meat} 0.001220132
## confidence coverage lift count
## [1] 0.3958333 0.004880529 35.71579 19
## [2] 0.2105263 0.005795628 26.20919 12
## [3] 0.3800000 0.005083884 22.92822 19
## [4] 0.4130435 0.004677173 21.49356 19
## [5] 0.6315789 0.001931876 18.99565 12Dari output diatas didapatkan 5 rule dengan menggunakan algoritma FP-Growth dimana nilai confidence tertinggi ada pada rule 5 sebesar 63.15% yang artinya tingkat kepercayaan seseorang untuk membeli instant food product, soda dan setelahnya membeli hamburger meat sebesar 63.15%. Nilai lift yang dihasilkan sebesar 18.99565 dimana ini lebih besar daripada 1 yang menandakan bahwa pada rule 5 terdapat hubungan antara setiap item LHS dan item RHS sehingga rule 5 cocok untuk dilakukan analisis. Kemudian diperoleh support sebesar 0.12% ini mengartikan bahwa pada rule 5, dari seluruh transaksi yang ada item instant food product, soda dan setelahnya membeli hamburger meat dibeli secara bersamaan sebanyak 12 kali dari total transaksi keseluruhan 9835. Begitupun untuk interpretasi pada rule lainnya.