library(arulesViz)
## Loading required package: arules
## Loading required package: Matrix
##
## Attaching package: 'arules'
## The following objects are masked from 'package:base':
##
## abbreviate, write
## Loading required package: grid
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
## 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 sausage
rules <- apriori(Groceries, 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 FALSE
##
## 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 [0.01s].
## writing ... [15 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
summary(rules)
## set of 15 rules
##
## rule length distribution (lhs + rhs):sizes
## 3
## 15
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3 3 3 3 3 3
##
## summary of quality measures:
## support confidence lift
## Min. :0.01007 Min. :0.5000 Min. :1.984
## 1st Qu.:0.01174 1st Qu.:0.5151 1st Qu.:2.036
## Median :0.01230 Median :0.5245 Median :2.203
## Mean :0.01316 Mean :0.5411 Mean :2.299
## 3rd Qu.:0.01403 3rd Qu.:0.5718 3rd Qu.:2.432
## Max. :0.02227 Max. :0.5862 Max. :3.030
##
## mining info:
## data ntransactions support confidence
## Groceries 9835 0.01 0.5
inspect(head(sort(rules, by ="lift"),10))
## lhs rhs support confidence lift
## [1] {citrus fruit,
## root vegetables} => {other vegetables} 0.01037112 0.5862069 3.029608
## [2] {tropical fruit,
## root vegetables} => {other vegetables} 0.01230300 0.5845411 3.020999
## [3] {root vegetables,
## rolls/buns} => {other vegetables} 0.01220132 0.5020921 2.594890
## [4] {root vegetables,
## yogurt} => {other vegetables} 0.01291307 0.5000000 2.584078
## [5] {curd,
## yogurt} => {whole milk} 0.01006609 0.5823529 2.279125
## [6] {other vegetables,
## butter} => {whole milk} 0.01148958 0.5736041 2.244885
## [7] {tropical fruit,
## root vegetables} => {whole milk} 0.01199797 0.5700483 2.230969
## [8] {root vegetables,
## yogurt} => {whole milk} 0.01453991 0.5629921 2.203354
## [9] {other vegetables,
## domestic eggs} => {whole milk} 0.01230300 0.5525114 2.162336
## [10] {yogurt,
## whipped/sour cream} => {whole milk} 0.01087951 0.5245098 2.052747
plot(rules, method="graph")
itemFrequencyPlot(Groceries,topN=20,type="absolute")
plot(rules[1:5],method="graph",interactive = F)
plot(rules[1:15],method="graph",interactive = T)
itemFrequencyPlot(Groceries,topN=20,type="absolute")
library(rattle)
## Rattle: A free graphical interface for data mining with R.
## Version 4.1.0 Copyright (c) 2006-2015 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.
ls()
## [1] "Groceries" "rules"
#
# setwd("C:/Users/Dell/Desktop")
# dir(pattern = 'csv')
# dvd=read.csv("dvd.csv")
#
# dvdrules <- apriori(dvd[,2], parameter=list(support=0.01, confidence=0.5))
library(arules)
a=read.transactions("http://fimi.ua.ac.be/data/retail.dat")
## Warning in rm(list = cmd, envir = .tkplot.env): object 'tkp.1' not found
itemFrequencyPlot(a,topN=20,type="absolute")
basket_rules <- apriori(a,parameter = list(sup = 0.01, conf = 0.01,target="rules"))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.01 0.1 1 none FALSE TRUE 5 0.01 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: 881
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[16470 item(s), 88162 transaction(s)] done [0.51s].
## sorting and recoding items ... [70 item(s)] done [0.02s].
## creating transaction tree ... done [0.11s].
## checking subsets of size 1 2 3 4 done [0.01s].
## writing ... [285 rule(s)] done [0.00s].
## creating S4 object ... done [0.01s].
inspect(basket_rules)[1:10,]
## lhs rhs support confidence lift
## [1] {} => {242} 0.01033325 0.01033325 1.0000000
## [2] {} => {45} 0.01033325 0.01033325 1.0000000
## [3] {} => {37} 0.01218212 0.01218212 1.0000000
## [4] {} => {161} 0.01145618 0.01145618 1.0000000
## [5] {} => {286} 0.01341848 0.01341848 1.0000000
## [6] {} => {175} 0.01100247 0.01100247 1.0000000
## [7] {} => {264} 0.01015177 0.01015177 1.0000000
## [8] {} => {2958} 0.01025385 0.01025385 1.0000000
## [9] {} => {13041} 0.01192124 0.01192124 1.0000000
## [10] {} => {31} 0.01043533 0.01043533 1.0000000
## [11] {} => {740} 0.01339579 0.01339579 1.0000000
## [12] {} => {956} 0.01033325 0.01033325 1.0000000
## [13] {} => {16217} 0.01322565 0.01322565 1.0000000
## [14] {} => {15832} 0.01296477 0.01296477 1.0000000
## [15] {} => {479} 0.01050339 0.01050339 1.0000000
## [16] {} => {78} 0.01202332 0.01202332 1.0000000
## [17] {} => {522} 0.01104784 0.01104784 1.0000000
## [18] {} => {258} 0.01119530 0.01119530 1.0000000
## [19] {} => {19} 0.01139947 0.01139947 1.0000000
## [20] {} => {179} 0.01132007 0.01132007 1.0000000
## [21] {} => {10515} 0.01000431 0.01000431 1.0000000
## [22] {} => {3270} 0.01077562 0.01077562 1.0000000
## [23] {} => {49} 0.01270389 0.01270389 1.0000000
## [24] {} => {589} 0.01269254 0.01269254 1.0000000
## [25] {} => {677} 0.01259046 0.01259046 1.0000000
## [26] {} => {16010} 0.01492707 0.01492707 1.0000000
## [27] {} => {1004} 0.01249972 0.01249972 1.0000000
## [28] {} => {783} 0.01094576 0.01094576 1.0000000
## [29] {} => {12925} 0.01663982 0.01663982 1.0000000
## [30] {} => {824} 0.01372473 0.01372473 1.0000000
## [31] {} => {548} 0.01289671 0.01289671 1.0000000
## [32] {} => {1146} 0.01617477 0.01617477 1.0000000
## [33] {} => {117} 0.01163767 0.01163767 1.0000000
## [34] {} => {592} 0.01391756 0.01391756 1.0000000
## [35] {} => {1393} 0.01316894 0.01316894 1.0000000
## [36] {} => {249} 0.01315760 0.01315760 1.0000000
## [37] {} => {338} 0.01445067 0.01445067 1.0000000
## [38] {} => {123} 0.01476827 0.01476827 1.0000000
## [39] {} => {604} 0.01371339 0.01371339 1.0000000
## [40] {} => {301} 0.01365668 0.01365668 1.0000000
## [41] {} => {14098} 0.01464350 0.01464350 1.0000000
## [42] {} => {79} 0.01814841 0.01814841 1.0000000
## [43] {} => {201} 0.01285134 0.01285134 1.0000000
## [44] {} => {1327} 0.02025816 0.02025816 1.0000000
## [45] {} => {438} 0.02113155 0.02113155 1.0000000
## [46] {} => {60} 0.01688936 0.01688936 1.0000000
## [47] {} => {255} 0.01671922 0.01671922 1.0000000
## [48] {} => {533} 0.01686668 0.01686668 1.0000000
## [49] {} => {185} 0.01560763 0.01560763 1.0000000
## [50] {} => {270} 0.01966834 0.01966834 1.0000000
## [51] {} => {9} 0.01556226 0.01556226 1.0000000
## [52] {} => {2238} 0.01945283 0.01945283 1.0000000
## [53] {} => {110} 0.03169166 0.03169166 1.0000000
## [54] {} => {147} 0.02017876 0.02017876 1.0000000
## [55] {} => {271} 0.02375173 0.02375173 1.0000000
## [56] {} => {413} 0.02132438 0.02132438 1.0000000
## [57] {} => {36} 0.03330233 0.03330233 1.0000000
## [58] {} => {475} 0.02457975 0.02457975 1.0000000
## [59] {} => {170} 0.03515120 0.03515120 1.0000000
## [60] {} => {101} 0.02537374 0.02537374 1.0000000
## [61] {} => {310} 0.02942311 0.02942311 1.0000000
## [62] {} => {237} 0.03439123 0.03439123 1.0000000
## [63] {} => {225} 0.03694335 0.03694335 1.0000000
## [64] {} => {89} 0.04352215 0.04352215 1.0000000
## [65] {} => {65} 0.05072480 0.05072480 1.0000000
## [66] {} => {38} 0.17690161 0.17690161 1.0000000
## [67] {} => {32} 0.17203557 0.17203557 1.0000000
## [68] {} => {41} 0.16951748 0.16951748 1.0000000
## [69] {} => {48} 0.47792700 0.47792700 1.0000000
## [70] {} => {39} 0.57479413 0.57479413 1.0000000
## [71] {37} => {38} 0.01186452 0.97392924 5.5054853
## [72] {38} => {37} 0.01186452 0.06706848 5.5054853
## [73] {286} => {38} 0.01265852 0.94336433 5.3327062
## [74] {38} => {286} 0.01265852 0.07155681 5.3327062
## [75] {12925} => {39} 0.01063950 0.63940014 1.1123985
## [76] {39} => {12925} 0.01063950 0.01851011 1.1123985
## [77] {1146} => {39} 0.01114993 0.68934081 1.1992830
## [78] {39} => {1146} 0.01114993 0.01939813 1.1992830
## [79] {79} => {48} 0.01012908 0.55812500 1.1678039
## [80] {48} => {79} 0.01012908 0.02119378 1.1678039
## [81] {79} => {39} 0.01260180 0.69437500 1.2080412
## [82] {39} => {79} 0.01260180 0.02192403 1.2080412
## [83] {1327} => {48} 0.01097979 0.54199328 1.1340504
## [84] {48} => {1327} 0.01097979 0.02297377 1.1340504
## [85] {1327} => {39} 0.01311223 0.64725644 1.1260665
## [86] {39} => {1327} 0.01311223 0.02281204 1.1260665
## [87] {438} => {48} 0.01162632 0.55018787 1.1511965
## [88] {48} => {438} 0.01162632 0.02432657 1.1511965
## [89] {438} => {39} 0.01429187 0.67632850 1.1766448
## [90] {39} => {438} 0.01429187 0.02486433 1.1766448
## [91] {60} => {39} 0.01114993 0.66017461 1.1485410
## [92] {39} => {60} 0.01114993 0.01939813 1.1485410
## [93] {255} => {48} 0.01198929 0.71709634 1.5004307
## [94] {48} => {255} 0.01198929 0.02508603 1.5004307
## [95] {255} => {39} 0.01198929 0.71709634 1.2475707
## [96] {39} => {255} 0.01198929 0.02085841 1.2475707
## [97] {533} => {39} 0.01045802 0.62004035 1.0787173
## [98] {39} => {533} 0.01045802 0.01819438 1.0787173
## [99] {270} => {48} 0.01085502 0.55190311 1.1547854
## [100] {48} => {270} 0.01085502 0.02271271 1.1547854
## [101] {270} => {39} 0.01354325 0.68858131 1.1979616
## [102] {39} => {270} 0.01354325 0.02356191 1.1979616
## [103] {2238} => {48} 0.01083233 0.55685131 1.1651388
## [104] {48} => {2238} 0.01083233 0.02266524 1.1651388
## [105] {2238} => {39} 0.01459813 0.75043732 1.3055758
## [106] {39} => {2238} 0.01459813 0.02539714 1.3055758
## [107] {110} => {38} 0.03090901 0.97530422 5.5132579
## [108] {38} => {110} 0.03090901 0.17472429 5.5132579
## [109] {110} => {48} 0.01565300 0.49391553 1.0334539
## [110] {48} => {110} 0.01565300 0.03275187 1.0334539
## [111] {110} => {39} 0.01995191 0.62956335 1.0952849
## [112] {39} => {110} 0.01995191 0.03471140 1.0952849
## [113] {147} => {48} 0.01175109 0.58234963 1.2184908
## [114] {48} => {147} 0.01175109 0.02458763 1.2184908
## [115] {147} => {39} 0.01289671 0.63912310 1.1119165
## [116] {39} => {147} 0.01289671 0.02243710 1.1119165
## [117] {271} => {48} 0.01236360 0.52053486 1.0891514
## [118] {48} => {271} 0.01236360 0.02586923 1.0891514
## [119] {271} => {39} 0.01626551 0.68481375 1.1914070
## [120] {39} => {271} 0.01626551 0.02829798 1.1914070
## [121] {413} => {48} 0.01287403 0.60372340 1.2632126
## [122] {48} => {413} 0.01287403 0.02693723 1.2632126
## [123] {413} => {39} 0.01281731 0.60106383 1.0457028
## [124] {39} => {413} 0.01281731 0.02229896 1.0457028
## [125] {36} => {38} 0.03164629 0.95027248 5.3717570
## [126] {38} => {36} 0.03164629 0.17889202 5.3717570
## [127] {36} => {48} 0.01606134 0.48228883 1.0091266
## [128] {48} => {36} 0.01606134 0.03360627 1.0091266
## [129] {36} => {39} 0.02310519 0.69380109 1.2070428
## [130] {39} => {36} 0.02310519 0.04019734 1.2070428
## [131] {475} => {48} 0.01619745 0.65897554 1.3788205
## [132] {48} => {475} 0.01619745 0.03389106 1.3788205
## [133] {475} => {39} 0.01701413 0.69220120 1.2042593
## [134] {39} => {475} 0.01701413 0.02960039 1.2042593
## [135] {170} => {38} 0.03437989 0.97805744 5.5288215
## [136] {38} => {170} 0.03437989 0.19434470 5.5288215
## [137] {170} => {48} 0.01766067 0.50242014 1.0512487
## [138] {48} => {170} 0.01766067 0.03695265 1.0512487
## [139] {170} => {39} 0.02335473 0.66440787 1.1559058
## [140] {39} => {170} 0.02335473 0.04063148 1.1559058
## [141] {101} => {48} 0.01487035 0.58605275 1.2262391
## [142] {48} => {101} 0.01487035 0.03111428 1.2262391
## [143] {101} => {39} 0.01587986 0.62583818 1.0888041
## [144] {39} => {101} 0.01587986 0.02762704 1.0888041
## [145] {310} => {48} 0.01919194 0.65227448 1.3647994
## [146] {48} => {310} 0.01919194 0.04015664 1.3647994
## [147] {310} => {39} 0.02100678 0.71395528 1.2421061
## [148] {39} => {310} 0.02100678 0.03654662 1.2421061
## [149] {237} => {48} 0.01907851 0.55474934 1.1607407
## [150] {48} => {237} 0.01907851 0.03991931 1.1607407
## [151] {237} => {39} 0.02188018 0.63621372 1.1068549
## [152] {39} => {237} 0.02188018 0.03806611 1.1068549
## [153] {225} => {48} 0.01969102 0.53300583 1.1152453
## [154] {48} => {225} 0.01969102 0.04120090 1.1152453
## [155] {225} => {39} 0.02666682 0.72182990 1.2558060
## [156] {39} => {225} 0.02666682 0.04639369 1.2558060
## [157] {89} => {48} 0.03173703 0.72921553 1.5257885
## [158] {48} => {89} 0.03173703 0.06640560 1.5257885
## [159] {89} => {39} 0.03118123 0.71644514 1.2464378
## [160] {39} => {89} 0.03118123 0.05424766 1.2464378
## [161] {65} => {41} 0.01128604 0.22249553 1.3125226
## [162] {41} => {65} 0.01128604 0.06657745 1.3125226
## [163] {65} => {48} 0.02868583 0.56551878 1.1832744
## [164] {48} => {65} 0.02868583 0.06002136 1.1832744
## [165] {65} => {39} 0.03161226 0.62321109 1.0842336
## [166] {39} => {65} 0.03161226 0.05499753 1.0842336
## [167] {38} => {32} 0.03213403 0.18164914 1.0558813
## [168] {32} => {38} 0.03213403 0.18678710 1.0558813
## [169] {38} => {41} 0.04420272 0.24987176 1.4740177
## [170] {41} => {38} 0.04420272 0.26075611 1.4740177
## [171] {38} => {48} 0.09010685 0.50936137 1.0657723
## [172] {48} => {38} 0.09010685 0.18853685 1.0657723
## [173] {38} => {39} 0.11734080 0.66331111 1.1539977
## [174] {39} => {38} 0.11734080 0.20414406 1.1539977
## [175] {32} => {41} 0.03625145 0.21072064 1.2430615
## [176] {41} => {32} 0.03625145 0.21385079 1.2430615
## [177] {32} => {48} 0.09112770 0.52970264 1.1083338
## [178] {48} => {32} 0.09112770 0.19067284 1.1083338
## [179] {32} => {39} 0.09590300 0.55746028 0.9698434
## [180] {39} => {32} 0.09590300 0.16684756 0.9698434
## [181] {41} => {48} 0.10228897 0.60341251 1.2625621
## [182] {48} => {41} 0.10228897 0.21402634 1.2625621
## [183] {41} => {39} 0.12946621 0.76373369 1.3287082
## [184] {39} => {41} 0.12946621 0.22523927 1.3287082
## [185] {48} => {39} 0.33055058 0.69163403 1.2032726
## [186] {39} => {48} 0.33055058 0.57507647 1.2032726
## [187] {110,38} => {48} 0.01543749 0.49944954 1.0450331
## [188] {110,48} => {38} 0.01543749 0.98623188 5.5750305
## [189] {38,48} => {110} 0.01543749 0.17132427 5.4059736
## [190] {110,38} => {39} 0.01973639 0.63853211 1.1108884
## [191] {110,39} => {38} 0.01973639 0.98919841 5.5917998
## [192] {38,39} => {110} 0.01973639 0.16819720 5.3073018
## [193] {110,48} => {39} 0.01176244 0.75144928 1.3073364
## [194] {110,39} => {48} 0.01176244 0.58953951 1.2335346
## [195] {39,48} => {110} 0.01176244 0.03558438 1.1228311
## [196] {36,38} => {48} 0.01542615 0.48745520 1.0199365
## [197] {36,48} => {38} 0.01542615 0.96045198 5.4293003
## [198] {38,48} => {36} 0.01542615 0.17119839 5.1407331
## [199] {36,38} => {39} 0.02206166 0.69713262 1.2128388
## [200] {36,39} => {38} 0.02206166 0.95483554 5.3975514
## [201] {38,39} => {36} 0.02206166 0.18801353 5.6456571
## [202] {36,48} => {39} 0.01265852 0.78813559 1.3711615
## [203] {36,39} => {48} 0.01265852 0.54786451 1.1463351
## [204] {39,48} => {36} 0.01265852 0.03829524 1.1499269
## [205] {475,48} => {39} 0.01238629 0.76470588 1.3303996
## [206] {39,475} => {48} 0.01238629 0.72800000 1.5232452
## [207] {39,48} => {475} 0.01238629 0.03747169 1.5244943
## [208] {170,38} => {48} 0.01744516 0.50742329 1.0617172
## [209] {170,48} => {38} 0.01744516 0.98779705 5.5838781
## [210] {38,48} => {170} 0.01744516 0.19360524 5.5077847
## [211] {170,38} => {39} 0.02290102 0.66611679 1.1588789
## [212] {170,39} => {38} 0.02290102 0.98057309 5.5430421
## [213] {38,39} => {170} 0.02290102 0.19516675 5.5522074
## [214] {170,48} => {39} 0.01367936 0.77456647 1.3475546
## [215] {170,39} => {48} 0.01367936 0.58572122 1.2255454
## [216] {39,48} => {170} 0.01367936 0.04138357 1.1773018
## [217] {101,48} => {39} 0.01073025 0.72158658 1.2553826
## [218] {101,39} => {48} 0.01073025 0.67571429 1.4138441
## [219] {39,48} => {101} 0.01073025 0.03246174 1.2793437
## [220] {310,48} => {39} 0.01527869 0.79609929 1.3850164
## [221] {310,39} => {48} 0.01527869 0.72732181 1.5218262
## [222] {39,48} => {310} 0.01527869 0.04622195 1.5709404
## [223] {237,48} => {39} 0.01411039 0.73959572 1.2867141
## [224] {237,39} => {48} 0.01411039 0.64489373 1.3493561
## [225] {39,48} => {237} 0.01411039 0.04268753 1.2412329
## [226] {225,48} => {39} 0.01587986 0.80645161 1.4030269
## [227] {225,39} => {48} 0.01587986 0.59549128 1.2459879
## [228] {39,48} => {225} 0.01587986 0.04804063 1.3003862
## [229] {48,89} => {39} 0.02410336 0.75947105 1.3212923
## [230] {39,89} => {48} 0.02410336 0.77300837 1.6174193
## [231] {39,48} => {89} 0.02410336 0.07291881 1.6754413
## [232] {48,65} => {39} 0.02038293 0.71055753 1.2361948
## [233] {39,65} => {48} 0.02038293 0.64477933 1.3491168
## [234] {39,48} => {65} 0.02038293 0.06166358 1.2156495
## [235] {32,38} => {48} 0.01867018 0.58100953 1.2156868
## [236] {38,48} => {32} 0.01867018 0.20720040 1.2044044
## [237] {32,48} => {38} 0.01867018 0.20487926 1.1581537
## [238] {32,38} => {39} 0.02087067 0.64948818 1.1299492
## [239] {38,39} => {32} 0.02087067 0.17786370 1.0338775
## [240] {32,39} => {38} 0.02087067 0.21762271 1.2301906
## [241] {38,41} => {48} 0.02692770 0.60918655 1.2746435
## [242] {38,48} => {41} 0.02692770 0.29884189 1.7628972
## [243] {41,48} => {38} 0.02692770 0.26325128 1.4881225
## [244] {38,41} => {39} 0.03460675 0.78290993 1.3620702
## [245] {38,39} => {41} 0.03460675 0.29492508 1.7397916
## [246] {39,41} => {38} 0.03460675 0.26730331 1.5110281
## [247] {38,48} => {39} 0.06921349 0.76812689 1.3363513
## [248] {38,39} => {48} 0.06921349 0.58985017 1.2341847
## [249] {39,48} => {38} 0.06921349 0.20938851 1.1836439
## [250] {32,41} => {48} 0.02340010 0.64549437 1.3506129
## [251] {32,48} => {41} 0.02340010 0.25678367 1.5147917
## [252] {41,48} => {32} 0.02340010 0.22876469 1.3297523
## [253] {32,41} => {39} 0.02675756 0.73811014 1.2841296
## [254] {32,39} => {41} 0.02675756 0.27900651 1.6458863
## [255] {39,41} => {32} 0.02675756 0.20667601 1.2013563
## [256] {32,48} => {39} 0.06127356 0.67239233 1.1697968
## [257] {32,39} => {48} 0.06127356 0.63891189 1.3368399
## [258] {39,48} => {32} 0.06127356 0.18536820 1.0774992
## [259] {41,48} => {39} 0.08355074 0.81681082 1.4210493
## [260] {39,41} => {48} 0.08355074 0.64534782 1.3503063
## [261] {39,48} => {41} 0.08355074 0.25276234 1.4910695
## [262] {110,38,48} => {39} 0.01169438 0.75753123 1.3179175
## [263] {110,38,39} => {48} 0.01169438 0.59252874 1.2397892
## [264] {110,39,48} => {38} 0.01169438 0.99421408 5.6201527
## [265] {38,39,48} => {110} 0.01169438 0.16896100 5.3314028
## [266] {36,38,48} => {39} 0.01225018 0.79411765 1.3815688
## [267] {36,38,39} => {48} 0.01225018 0.55526992 1.1618300
## [268] {36,39,48} => {38} 0.01225018 0.96774194 5.4705094
## [269] {38,39,48} => {36} 0.01225018 0.17699115 5.3146777
## [270] {170,38,48} => {39} 0.01353191 0.77568270 1.3494966
## [271] {170,38,39} => {48} 0.01353191 0.59088658 1.2363532
## [272] {170,39,48} => {38} 0.01353191 0.98922056 5.5919251
## [273] {38,39,48} => {170} 0.01353191 0.19550967 5.5619630
## [274] {32,38,48} => {39} 0.01401965 0.75091130 1.3064004
## [275] {32,38,39} => {48} 0.01401965 0.67173913 1.4055266
## [276] {38,39,48} => {32} 0.01401965 0.20255654 1.1774108
## [277] {32,39,48} => {38} 0.01401965 0.22880415 1.2933977
## [278] {38,41,48} => {39} 0.02258343 0.83866891 1.4590770
## [279] {38,39,41} => {48} 0.02258343 0.65257293 1.3654239
## [280] {38,39,48} => {41} 0.02258343 0.32628646 1.9247954
## [281] {39,41,48} => {38} 0.02258343 0.27029595 1.5279451
## [282] {32,41,48} => {39} 0.01867018 0.79786718 1.3880921
## [283] {32,39,41} => {48} 0.01867018 0.69775329 1.4599579
## [284] {32,39,48} => {41} 0.01867018 0.30470196 1.7974663
## [285] {39,41,48} => {32} 0.01867018 0.22345914 1.2989124
## lhs rhs support confidence lift
## [1] {} => {242} 0.01033325 0.01033325 1
## [2] {} => {45} 0.01033325 0.01033325 1
## [3] {} => {37} 0.01218212 0.01218212 1
## [4] {} => {161} 0.01145618 0.01145618 1
## [5] {} => {286} 0.01341848 0.01341848 1
## [6] {} => {175} 0.01100247 0.01100247 1
## [7] {} => {264} 0.01015177 0.01015177 1
## [8] {} => {2958} 0.01025385 0.01025385 1
## [9] {} => {13041} 0.01192124 0.01192124 1
## [10] {} => {31} 0.01043533 0.01043533 1
basket_rules<- sort(basket_rules, by="lift")
library(arulesViz)
plot(basket_rules[1:10,])
plot(basket_rules[1:10,], method="graph", control=list(type="items"))
