Apriori Algorithm
Anvesh Baru
7/21/2019
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#Association Rules - Apriori algorithm #
# Input - Grocery Store Transactions#
# install association rules package
library('arules')## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary("knitr")
# install association rules Visualization package
library('arulesViz')## Loading required package: grid## Registered S3 method overwritten by 'seriation':
## method from
## reorder.hclust gclusdata(Groceries)
summary(Groceries) # indicates 9835 rows## 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
## 2159 1643 1299 1005 855 645 545 438 350 246 182 117 78 77 55
## 16 17 18 19 20 21 22 23 24 26 27 28 29 32
## 46 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 sausageclass(Groceries)## [1] "transactions"
## attr(,"package")
## [1] "arules"Groceries@itemInfo[1:20,]## labels level2 level1
## 1 frankfurter sausage meat and sausage
## 2 sausage sausage meat and sausage
## 3 liver loaf sausage meat and sausage
## 4 ham sausage meat and sausage
## 5 meat sausage meat and sausage
## 6 finished products sausage meat and sausage
## 7 organic sausage sausage meat and sausage
## 8 chicken poultry meat and sausage
## 9 turkey poultry meat and sausage
## 10 pork pork meat and sausage
## 11 beef beef meat and sausage
## 12 hamburger meat beef meat and sausage
## 13 fish fish meat and sausage
## 14 citrus fruit fruit fruit and vegetables
## 15 tropical fruit fruit fruit and vegetables
## 16 pip fruit fruit fruit and vegetables
## 17 grapes fruit fruit and vegetables
## 18 berries fruit fruit and vegetables
## 19 nuts/prunes fruit fruit and vegetables
## 20 root vegetables vegetables fruit and vegetables#The following code displays the lOth to 20th transactions of the Groceries dataset.
apply(Groceries@data[,10:20],2,function(r) paste(Groceries@itemInfo[r,"labels"],collapse=", "))## [1] "whole milk, cereals"
## [2] "tropical fruit, other vegetables, white bread, bottled water, chocolate"
## [3] "citrus fruit, tropical fruit, whole milk, butter, curd, yogurt, flour, bottled water, dishes"
## [4] "beef"
## [5] "frankfurter, rolls/buns, soda"
## [6] "chicken, tropical fruit"
## [7] "butter, sugar, fruit/vegetable juice, newspapers"
## [8] "fruit/vegetable juice"
## [9] "packaged fruit/vegetables"
## [10] "chocolate"
## [11] "specialty bar"#Apriori algorithm Implementation below
# First, get itemsets of length 1
itemsets<-apriori(Groceries,parameter=list(minlen=1,maxlen=1,support=0.02,target="frequent itemsets"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## NA 0.1 1 none FALSE TRUE 5 0.02 1
## maxlen target ext
## 1 frequent itemsets FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 196
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
## sorting and recoding items ... [59 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1## Warning in apriori(Groceries, parameter = list(minlen = 1, maxlen = 1,
## support = 0.02, : Mining stopped (maxlen reached). Only patterns up to a
## length of 1 returned!## done [0.00s].
## writing ... [59 set(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(itemsets) # found 59 itemsets## set of 59 itemsets
##
## most frequent items:
## frankfurter sausage ham meat chicken (Other)
## 1 1 1 1 1 54
##
## element (itemset/transaction) length distribution:sizes
## 1
## 59
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1 1 1 1 1
##
## summary of quality measures:
## support count
## Min. :0.02105 Min. : 207.0
## 1st Qu.:0.03015 1st Qu.: 296.5
## Median :0.04809 Median : 473.0
## Mean :0.06200 Mean : 609.8
## 3rd Qu.:0.07666 3rd Qu.: 754.0
## Max. :0.25552 Max. :2513.0
##
## includes transaction ID lists: FALSE
##
## mining info:
## data ntransactions support confidence
## Groceries 9835 0.02 1inspect(head(sort(itemsets,by="support"),10)) # lists top 10## items support count
## [1] {whole milk} 0.25551601 2513
## [2] {other vegetables} 0.19349263 1903
## [3] {rolls/buns} 0.18393493 1809
## [4] {soda} 0.17437722 1715
## [5] {yogurt} 0.13950178 1372
## [6] {bottled water} 0.11052364 1087
## [7] {root vegetables} 0.10899847 1072
## [8] {tropical fruit} 0.10493137 1032
## [9] {shopping bags} 0.09852567 969
## [10] {sausage} 0.09395018 924# Second, get itemsets of length 2
itemsets<-apriori(Groceries,parameter=list(minlen=2,maxlen=2,support=0.02,target="frequent itemsets"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## NA 0.1 1 none FALSE TRUE 5 0.02 2
## maxlen target ext
## 2 frequent itemsets FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 196
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [59 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2## Warning in apriori(Groceries, parameter = list(minlen = 2, maxlen = 2,
## support = 0.02, : Mining stopped (maxlen reached). Only patterns up to a
## length of 2 returned!## done [0.00s].
## writing ... [61 set(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(itemsets) # found 61 itemsets## set of 61 itemsets
##
## most frequent items:
## whole milk other vegetables yogurt rolls/buns
## 25 17 9 9
## soda (Other)
## 9 53
##
## element (itemset/transaction) length distribution:sizes
## 2
## 61
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 2 2 2 2 2
##
## summary of quality measures:
## support count
## Min. :0.02003 Min. :197.0
## 1st Qu.:0.02227 1st Qu.:219.0
## Median :0.02613 Median :257.0
## Mean :0.02951 Mean :290.3
## 3rd Qu.:0.03223 3rd Qu.:317.0
## Max. :0.07483 Max. :736.0
##
## includes transaction ID lists: FALSE
##
## mining info:
## data ntransactions support confidence
## Groceries 9835 0.02 1inspect(head(sort(itemsets,by="support"),10)) # lists top 10## items support count
## [1] {other vegetables,whole milk} 0.07483477 736
## [2] {whole milk,rolls/buns} 0.05663447 557
## [3] {whole milk,yogurt} 0.05602440 551
## [4] {root vegetables,whole milk} 0.04890696 481
## [5] {root vegetables,other vegetables} 0.04738180 466
## [6] {other vegetables,yogurt} 0.04341637 427
## [7] {other vegetables,rolls/buns} 0.04260295 419
## [8] {tropical fruit,whole milk} 0.04229792 416
## [9] {whole milk,soda} 0.04006101 394
## [10] {rolls/buns,soda} 0.03833249 377# Third, get itemsets of length 3
itemsets<-apriori(Groceries,parameter=list(minlen=3,maxlen=3,support=0.02,target="frequent itemsets"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## NA 0.1 1 none FALSE TRUE 5 0.02 3
## maxlen target ext
## 3 frequent itemsets FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 196
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [59 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3## Warning in apriori(Groceries, parameter = list(minlen = 3, maxlen = 3,
## support = 0.02, : Mining stopped (maxlen reached). Only patterns up to a
## length of 3 returned!## done [0.00s].
## writing ... [2 set(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(itemsets) # found 2 itemsets## set of 2 itemsets
##
## most frequent items:
## other vegetables whole milk root vegetables yogurt
## 2 2 1 1
## frankfurter (Other)
## 0 0
##
## element (itemset/transaction) length distribution:sizes
## 3
## 2
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3 3 3 3 3 3
##
## summary of quality measures:
## support count
## Min. :0.02227 Min. :219.0
## 1st Qu.:0.02250 1st Qu.:221.2
## Median :0.02272 Median :223.5
## Mean :0.02272 Mean :223.5
## 3rd Qu.:0.02295 3rd Qu.:225.8
## Max. :0.02318 Max. :228.0
##
## includes transaction ID lists: FALSE
##
## mining info:
## data ntransactions support confidence
## Groceries 9835 0.02 1inspect(head(sort(itemsets,by="support"),10)) # lists top 10## items support count
## [1] {root vegetables,other vegetables,whole milk} 0.02318251 228
## [2] {other vegetables,whole milk,yogurt} 0.02226741 219# Fourth, get itemsets of length 4
itemsets<-apriori(Groceries,parameter=list(minlen=4,maxlen=4,support=0.02,target="frequent itemsets"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## NA 0.1 1 none FALSE TRUE 5 0.02 4
## maxlen target ext
## 4 frequent itemsets FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 196
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [59 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [0 set(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(itemsets) # found 2 itemsets## set of 0 itemsetsinspect(head(sort(itemsets,by="support"),10)) # lists top 10, No frequent 4-itemsets have been found, and the algorithm converges.
# The Apriori function () is used to generate rules. A threshold is set lower than 0.001 and minimum confidence threshold is set to 0.6. Below code generates 2,918 rules.
rules <- apriori(Groceries,parameter=list(support=0.001,confidence=0.6,target="rules"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.6 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 10 rules FALSE
##
## 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 [0.00s].
## sorting and recoding items ... [157 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [0.01s].
## writing ... [2918 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(rules) # finds 2918 rules## set of 2918 rules
##
## rule length distribution (lhs + rhs):sizes
## 2 3 4 5 6
## 3 490 1765 626 34
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 4.000 4.000 4.068 4.000 6.000
##
## summary of quality measures:
## support confidence lift count
## Min. :0.001017 Min. :0.6000 Min. : 2.348 Min. :10.00
## 1st Qu.:0.001118 1st Qu.:0.6316 1st Qu.: 2.668 1st Qu.:11.00
## Median :0.001220 Median :0.6818 Median : 3.168 Median :12.00
## Mean :0.001480 Mean :0.7028 Mean : 3.450 Mean :14.55
## 3rd Qu.:0.001525 3rd Qu.:0.7500 3rd Qu.: 3.692 3rd Qu.:15.00
## Max. :0.009354 Max. :1.0000 Max. :18.996 Max. :92.00
##
## mining info:
## data ntransactions support confidence
## Groceries 9835 0.001 0.6plot(rules) # displays scatterplot## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
# Compute the 1/Support(Y) ie slope
slope<-sort(round(rules@quality$lift/rules@quality$confidence,2))
#Display the number of times each slope appears in dataset
unlist(lapply(split(slope,f=slope),length))## 3.91 5.17 5.44 5.73 7.17 9.05 9.17 9.53 10.64 12.08 12.42 13.22
## 1585 940 12 7 188 1 102 55 1 4 1 5
## 13.83 13.95 18.05 23.76 26.44 30.08
## 2 9 3 1 1 1#Inspect function is used to display the top 10 rules sorted by lift
inspect(head(sort(rules,by="lift"),10))## lhs rhs support confidence lift count
## [1] {Instant food products,
## soda} => {hamburger meat} 0.001220132 0.6315789 18.995654 12
## [2] {soda,
## popcorn} => {salty snack} 0.001220132 0.6315789 16.697793 12
## [3] {ham,
## processed cheese} => {white bread} 0.001931876 0.6333333 15.045491 19
## [4] {tropical fruit,
## other vegetables,
## yogurt,
## white bread} => {butter} 0.001016777 0.6666667 12.030581 10
## [5] {hamburger meat,
## yogurt,
## whipped/sour cream} => {butter} 0.001016777 0.6250000 11.278670 10
## [6] {tropical fruit,
## other vegetables,
## whole milk,
## yogurt,
## domestic eggs} => {butter} 0.001016777 0.6250000 11.278670 10
## [7] {liquor,
## red/blush wine} => {bottled beer} 0.001931876 0.9047619 11.235269 19
## [8] {other vegetables,
## butter,
## sugar} => {whipped/sour cream} 0.001016777 0.7142857 9.964539 10
## [9] {whole milk,
## butter,
## hard cheese} => {whipped/sour cream} 0.001423488 0.6666667 9.300236 14
## [10] {tropical fruit,
## other vegetables,
## butter,
## fruit/vegetable juice} => {whipped/sour cream} 0.001016777 0.6666667 9.300236 10#Below code fetchces rules with confidence above 0.9
confidentRules<-rules[quality(rules)$confidence>0.9]
confidentRules # set of 127 rules## set of 127 rules#Plot a matrix-based visualization of the LHS v RHS of rules. This produces a Matrix-based visualization of LHS and RHS, colored by lift and confidence,
plot(confidentRules,method="matrix",measure=c("lift","confidence"),control=list(reorder="none"))## Itemsets in Antecedent (LHS)
## [1] "{liquor,red/blush wine}"
## [2] "{curd,cereals}"
## [3] "{soups,bottled beer}"
## [4] "{whipped/sour cream,house keeping products}"
## [5] "{pastry,sweet spreads}"
## [6] "{rice,sugar}"
## [7] "{rice,bottled water}"
## [8] "{canned fish,hygiene articles}"
## [9] "{grapes,onions}"
## [10] "{hard cheese,oil}"
## [11] "{root vegetables,butter,rice}"
## [12] "{herbs,whole milk,fruit/vegetable juice}"
## [13] "{citrus fruit,tropical fruit,herbs}"
## [14] "{root vegetables,whipped/sour cream,flour}"
## [15] "{butter,soft cheese,domestic eggs}"
## [16] "{tropical fruit,whipped/sour cream,soft cheese}"
## [17] "{root vegetables,whipped/sour cream,soft cheese}"
## [18] "{citrus fruit,root vegetables,soft cheese}"
## [19] "{frankfurter,tropical fruit,frozen meals}"
## [20] "{tropical fruit,butter,frozen meals}"
## [21] "{tropical fruit,whipped/sour cream,hard cheese}"
## [22] "{pork,whole milk,butter milk}"
## [23] "{pip fruit,butter milk,fruit/vegetable juice}"
## [24] "{frankfurter,root vegetables,sliced cheese}"
## [25] "{butter,whipped/sour cream,sliced cheese}"
## [26] "{yogurt,oil,coffee}"
## [27] "{root vegetables,onions,napkins}"
## [28] "{sausage,berries,butter}"
## [29] "{hamburger meat,tropical fruit,whipped/sour cream}"
## [30] "{butter,hygiene articles,napkins}"
## [31] "{pip fruit,butter,hygiene articles}"
## [32] "{tropical fruit,butter,hygiene articles}"
## [33] "{tropical fruit,domestic eggs,hygiene articles}"
## [34] "{root vegetables,whipped/sour cream,hygiene articles}"
## [35] "{pip fruit,root vegetables,hygiene articles}"
## [36] "{cream cheese ,domestic eggs,sugar}"
## [37] "{other vegetables,cream cheese ,sugar}"
## [38] "{curd,domestic eggs,sugar}"
## [39] "{citrus fruit,domestic eggs,sugar}"
## [40] "{tropical fruit,domestic eggs,sugar}"
## [41] "{yogurt,domestic eggs,sugar}"
## [42] "{root vegetables,whipped/sour cream,sugar}"
## [43] "{pork,rolls/buns,waffles}"
## [44] "{whipped/sour cream,long life bakery product,napkins}"
## [45] "{tropical fruit,long life bakery product,napkins}"
## [46] "{sausage,butter,long life bakery product}"
## [47] "{tropical fruit,dessert,whipped/sour cream}"
## [48] "{cream cheese ,domestic eggs,napkins}"
## [49] "{root vegetables,butter,cream cheese }"
## [50] "{pip fruit,whipped/sour cream,cream cheese }"
## [51] "{sausage,pip fruit,cream cheese }"
## [52] "{citrus fruit,root vegetables,cream cheese }"
## [53] "{root vegetables,butter,white bread}"
## [54] "{butter,whipped/sour cream,coffee}"
## [55] "{root vegetables,domestic eggs,coffee}"
## [56] "{butter,curd,domestic eggs}"
## [57] "{citrus fruit,butter,curd}"
## [58] "{domestic eggs,margarine,bottled beer}"
## [59] "{pip fruit,whipped/sour cream,brown bread}"
## [60] "{domestic eggs,margarine,fruit/vegetable juice}"
## [61] "{butter,whipped/sour cream,soda}"
## [62] "{pip fruit,butter,pastry}"
## [63] "{tropical fruit,whipped/sour cream,fruit/vegetable juice}"
## [64] "{root vegetables,other vegetables,yogurt,rice}"
## [65] "{root vegetables,whole milk,yogurt,rice}"
## [66] "{tropical fruit,root vegetables,herbs,other vegetables}"
## [67] "{tropical fruit,grapes,whole milk,yogurt}"
## [68] "{tropical fruit,pip fruit,yogurt,frozen meals}"
## [69] "{root vegetables,other vegetables,yogurt,hard cheese}"
## [70] "{ham,tropical fruit,pip fruit,yogurt}"
## [71] "{ham,tropical fruit,pip fruit,whole milk}"
## [72] "{tropical fruit,butter,yogurt,sliced cheese}"
## [73] "{tropical fruit,whole milk,butter,sliced cheese}"
## [74] "{tropical fruit,root vegetables,yogurt,oil}"
## [75] "{root vegetables,other vegetables,yogurt,oil}"
## [76] "{root vegetables,whole milk,yogurt,oil}"
## [77] "{root vegetables,other vegetables,yogurt,waffles}"
## [78] "{other vegetables,curd,whipped/sour cream,cream cheese }"
## [79] "{citrus fruit,whole milk,whipped/sour cream,cream cheese }"
## [80] "{pip fruit,root vegetables,other vegetables,cream cheese }"
## [81] "{pip fruit,other vegetables,yogurt,cream cheese }"
## [82] "{tropical fruit,butter,yogurt,white bread}"
## [83] "{tropical fruit,other vegetables,butter,white bread}"
## [84] "{root vegetables,other vegetables,butter,white bread}"
## [85] "{root vegetables,whole milk,butter,white bread}"
## [86] "{citrus fruit,other vegetables,yogurt,frozen vegetables}"
## [87] "{beef,tropical fruit,yogurt,rolls/buns}"
## [88] "{tropical fruit,curd,yogurt,domestic eggs}"
## [89] "{citrus fruit,tropical fruit,curd,yogurt}"
## [90] "{other vegetables,butter,whipped/sour cream,napkins}"
## [91] "{pork,other vegetables,butter,whipped/sour cream}"
## [92] "{pork,root vegetables,other vegetables,butter}"
## [93] "{frankfurter,tropical fruit,root vegetables,yogurt}"
## [94] "{pip fruit,root vegetables,other vegetables,brown bread}"
## [95] "{root vegetables,other vegetables,rolls/buns,brown bread}"
## [96] "{other vegetables,butter,whipped/sour cream,domestic eggs}"
## [97] "{tropical fruit,butter,yogurt,domestic eggs}"
## [98] "{root vegetables,butter,yogurt,domestic eggs}"
## [99] "{tropical fruit,butter,whipped/sour cream,fruit/vegetable juice}"
## [100] "{whole milk,butter,whipped/sour cream,soda}"
## [101] "{citrus fruit,other vegetables,butter,bottled water}"
## [102] "{whole milk,rolls/buns,soda,newspapers}"
## [103] "{pip fruit,other vegetables,whipped/sour cream,domestic eggs}"
## [104] "{citrus fruit,whole milk,whipped/sour cream,domestic eggs}"
## [105] "{tropical fruit,yogurt,whipped/sour cream,domestic eggs}"
## [106] "{tropical fruit,other vegetables,whipped/sour cream,domestic eggs}"
## [107] "{citrus fruit,tropical fruit,other vegetables,domestic eggs}"
## [108] "{tropical fruit,yogurt,whipped/sour cream,fruit/vegetable juice}"
## [109] "{tropical fruit,whole milk,whipped/sour cream,fruit/vegetable juice}"
## [110] "{pip fruit,root vegetables,yogurt,fruit/vegetable juice}"
## [111] "{citrus fruit,other vegetables,soda,fruit/vegetable juice}"
## [112] "{citrus fruit,whipped/sour cream,rolls/buns,pastry}"
## [113] "{citrus fruit,tropical fruit,root vegetables,whipped/sour cream}"
## [114] "{pip fruit,root vegetables,other vegetables,bottled water}"
## [115] "{tropical fruit,root vegetables,yogurt,pastry}"
## [116] "{sausage,tropical fruit,root vegetables,yogurt}"
## [117] "{sausage,tropical fruit,root vegetables,rolls/buns}"
## [118] "{tropical fruit,root vegetables,rolls/buns,bottled water}"
## [119] "{tropical fruit,root vegetables,other vegetables,yogurt,oil}"
## [120] "{tropical fruit,root vegetables,whole milk,yogurt,oil}"
## [121] "{tropical fruit,other vegetables,whole milk,yogurt,oil}"
## [122] "{tropical fruit,other vegetables,butter,yogurt,domestic eggs}"
## [123] "{citrus fruit,root vegetables,whole milk,yogurt,whipped/sour cream}"
## [124] "{citrus fruit,tropical fruit,root vegetables,whole milk,yogurt}"
## Itemsets in Consequent (RHS)
## [1] "{bottled beer}" "{whole milk}" "{other vegetables}"
## [4] "{yogurt}" "{root vegetables}"
#Visualize the top 5 rules with the highest lift and plot them
highLiftRules<-head(sort(rules,by="lift"),5)
plot(highLiftRules,method="graph",control=list(type="items"))## Warning: Unknown control parameters: type## Available control parameters (with default values):
## main = Graph for 5 rules
## nodeColors = c("#66CC6680", "#9999CC80")
## nodeCol = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
## edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
## alpha = 0.5
## cex = 1
## itemLabels = TRUE
## labelCol = #000000B3
## measureLabels = FALSE
## precision = 3
## layout = NULL
## layoutParams = list()
## arrowSize = 0.5
## engine = igraph
## plot = TRUE
## plot_options = list()
## max = 100
## verbose = FALSE