[연관규칙] 장바구니 분석을 위한 연관 규칙
Author : tmlab / Date : 2016. 10. 6. 17:07 / Category : Text Mining/R
12.asso_rules
kimsungguen
2015년 12월 4일
Association Rules for Market Basket Analysis
- 구매와 동시발생 자료로 부터 인사이트를 얻는 전략에 대해 연구할것이다.
- 연관규칙분석은 광범위한 데이터셋에서 정보가를 가진 패턴셋을 찾는 접근이다.
- 연관규칙분석을 활용해 80,000개 장바구니의 16000개 아이템에 대한 분석을 실시하겠다.
1. The Basics of Association Rules
- 연관규칙은 기대치 보다 더 많은 빈도로 특정 물건이 함께 구매된 패턴을 찾는것이다.
- R package arules 사용할 것이다.
- summary() 함수는 169물품들의 9835개 거래를 보여준다.
- inspect(head(Groceries)) 장바구니의 물건들을 볼수 있다.
2. Retail Transaction Data: Market Baskets
library(arules)## Warning: package 'arules' was built under R version 3.2.2## Loading required package: Matrix
##
## Attaching package: 'arules'
##
## The following objects are masked from 'package:base':
##
## %in%, abbreviate, writelibrary(arulesViz)## Warning: package 'arulesViz' was built under R version 3.2.2## Loading required package: grid
##
## Attaching package: 'arulesViz'
##
## The following object is masked from 'package:arules':
##
## abbreviate
##
## The following object is masked from 'package:base':
##
## abbreviatedata("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 meet and sausage
## 2 sausage sausage meet and sausage
## 3 liver loaf sausage meet and sausageinspect(head(Groceries, 10))## 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}
## 6 {whole milk,
## butter,
## yogurt,
## rice,
## abrasive cleaner}
## 7 {rolls/buns}
## 8 {other vegetables,
## UHT-milk,
## rolls/buns,
## bottled beer,
## liquor (appetizer)}
## 9 {pot plants}
## 10 {whole milk,
## cereals}- apriori(data, parameters=…) 함수는 apriori알고리즘으로 연관규칙을 찾아준다.
- apriori알고리즘은 transactions 리스트중에서 빈번하게 발생하는 아이템 셋을 찾는다.
- parameter=list()사용하여 최소지지도와 최소 신뢰도 값을 조절할수 있다.
- apriori() 결과해석시 2가지가 핵심이다.
- 첫째는 sorting and recoding items 결과에서 보여지는 규칙안에 몇개의 아이템이 있는지 보는것이다.
- 이 사례에서는 88개 아이템이 발견된다.
- 아이템이 너무많거나 적으면 지지도와 신뢰도 수준을 조정해야한다.
- 두번째는 writing 결과에서 보여지는 규칙의 수를 확인하는것이다. 이사례에서는 125개 룰이 발견된다.
- 규칙의 수가 너무 적으면 지지도와 신뢰도수준을 낮춰야한다. 규칙의 수가너무 많으면 지지도와 신뢰도를 높여야한다.
groc.rules <- apriori(Groceries, parameter=list(supp=0.01, conf=0.3, target="rules"))##
## Parameter specification:
## confidence minval smax arem aval originalSupport support minlen maxlen
## 0.3 0.1 1 none FALSE TRUE 0.01 1 10
## target ext
## rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## apriori - find association rules with the apriori algorithm
## version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt
## 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 ... [125 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].- 첫번째규칙에서 {beef}와 {root vegetables}규칙이 발견딘다. 1.7%지지도(장바구니의 1.7에서 해당 조합이 발생), 3.04리프트(단독으로 판매되는것보다 3배더 잘발생)함을 보여준다.
- 이는 가게에서 제품진열이나 쿠폰발행시 고려할수있음
inspect(subset(groc.rules, lift > 3))## lhs rhs support confidence lift
## 1 {beef} => {root vegetables} 0.01738688 0.3313953 3.040367
## 2 {citrus fruit,
## root vegetables} => {other vegetables} 0.01037112 0.5862069 3.029608
## 3 {citrus fruit,
## other vegetables} => {root vegetables} 0.01037112 0.3591549 3.295045
## 4 {tropical fruit,
## root vegetables} => {other vegetables} 0.01230300 0.5845411 3.020999
## 5 {tropical fruit,
## other vegetables} => {root vegetables} 0.01230300 0.3427762 3.1447802.2 Supermarket Data
- 상품에 대한 설명없이 상품번호로 구성된 바스켓 데이터
- 각 행은 1개의 바스켓에 담긴 물건, 88,162거래
retail.raw <- readLines("http://goo.gl/FfjDAO")
head(retail.raw)## [1] "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 "
## [2] "30 31 32 "
## [3] "33 34 35 "
## [4] "36 37 38 39 40 41 42 43 44 45 46 "
## [5] "38 39 47 48 "
## [6] "38 39 48 49 50 51 52 53 54 55 56 57 58 "tail(retail.raw)## [1] "48 201 255 278 407 479 767 824 986 1395 1598 2022 2283 2375 6725 13334 14006 14099 "
## [2] "39 875 2665 2962 12959 14070 14406 15518 16379 "
## [3] "39 41 101 346 393 413 479 522 586 635 695 799 1466 1786 1994 2449 2830 3035 3591 3722 6217 11493 12129 13033 "
## [4] "2310 4267 "
## [5] "39 48 2528 "
## [6] "32 39 205 242 1393 "summary(retail.raw)## Length Class Mode
## 88162 character character- 이런 텍스트형식의 데이터는 분석을 할수 없음
- strsplit(lines," “)함수로 텍스트를 개별아이템으로 분리
retail.list <- strsplit(retail.raw, " ")
names(retail.list) <- paste("Trans", 1:length(retail.list), sep="")
head(retail.list)## $Trans1
## [1] "0" "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13"
## [15] "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27"
## [29] "28" "29"
##
## $Trans2
## [1] "30" "31" "32"
##
## $Trans3
## [1] "33" "34" "35"
##
## $Trans4
## [1] "36" "37" "38" "39" "40" "41" "42" "43" "44" "45" "46"
##
## $Trans5
## [1] "38" "39" "47" "48"
##
## $Trans6
## [1] "38" "39" "48" "49" "50" "51" "52" "53" "54" "55" "56" "57" "58"- 데이터 확인
library(car)##
## Attaching package: 'car'
##
## The following object is masked from 'package:arules':
##
## recodestr(retail.list)## List of 88162
## $ Trans1 : chr [1:30] "0" "1" "2" "3" ...
## $ Trans2 : chr [1:3] "30" "31" "32"
## $ Trans3 : chr [1:3] "33" "34" "35"
## $ Trans4 : chr [1:11] "36" "37" "38" "39" ...
## $ Trans5 : chr [1:4] "38" "39" "47" "48"
## $ Trans6 : chr [1:13] "38" "39" "48" "49" ...
## $ Trans7 : chr [1:6] "32" "41" "59" "60" ...
## $ Trans8 : chr [1:3] "3" "39" "48"
## $ Trans9 : chr [1:6] "63" "64" "65" "66" ...
## $ Trans10 : chr [1:2] "32" "69"
## $ Trans11 : chr [1:4] "48" "70" "71" "72"
## $ Trans12 : chr [1:8] "39" "73" "74" "75" ...
## $ Trans13 : chr [1:8] "36" "38" "39" "41" ...
## $ Trans14 : chr [1:3] "82" "83" "84"
## $ Trans15 : chr [1:5] "41" "85" "86" "87" ...
## $ Trans16 : chr [1:15] "39" "48" "89" "90" ...
## $ Trans17 : chr [1:5] "36" "38" "39" "48" ...
## $ Trans18 : chr [1:9] "39" "41" "102" "103" ...
## $ Trans19 : chr [1:5] "38" "39" "41" "109" ...
## $ Trans20 : chr [1:9] "39" "111" "112" "113" ...
## $ Trans21 : chr [1:15] "119" "120" "121" "122" ...
## $ Trans22 : chr [1:4] "48" "134" "135" "136"
## $ Trans23 : chr [1:15] "39" "48" "137" "138" ...
## $ Trans24 : chr [1:4] "39" "150" "151" "152"
## $ Trans25 : chr [1:6] "38" "39" "56" "153" ...
## $ Trans26 : chr [1:6] "48" "156" "157" "158" ...
## $ Trans27 : chr [1:3] "39" "41" "48"
## $ Trans28 : chr [1:7] "161" "162" "163" "164" ...
## $ Trans29 : chr [1:9] "38" "39" "48" "168" ...
## $ Trans30 : chr [1:9] "32" "39" "41" "48" ...
## $ Trans31 : chr [1:10] "32" "38" "39" "47" ...
## $ Trans32 : chr [1:4] "39" "184" "185" "186"
## $ Trans33 : chr [1:7] "36" "38" "41" "48" ...
## $ Trans34 : chr [1:15] "39" "48" "186" "189" ...
## $ Trans35 : chr [1:10] "39" "201" "202" "203" ...
## $ Trans36 : chr [1:9] "39" "65" "193" "210" ...
## $ Trans37 : chr [1:10] "179" "216" "217" "218" ...
## $ Trans38 : chr [1:3] "225" "226" "227"
## $ Trans39 : chr [1:7] "39" "41" "48" "228" ...
## $ Trans40 : chr [1:14] "36" "38" "39" "232" ...
## $ Trans41 : chr [1:4] "39" "243" "244" "245"
## $ Trans42 : chr [1:8] "39" "41" "48" "246" ...
## $ Trans43 : chr [1:6] "39" "48" "65" "251" ...
## $ Trans44 : chr [1:3] "48" "230" "254"
## $ Trans45 : chr [1:12] "39" "48" "66" "78" ...
## $ Trans46 : chr [1:3] "39" "48" "262"
## $ Trans47 : chr [1:9] "36" "38" "39" "225" ...
## $ Trans48 : chr [1:6] "39" "242" "268" "269" ...
## $ Trans49 : chr [1:8] "39" "48" "79" "146" ...
## $ Trans50 : chr "274"
## $ Trans51 : chr [1:13] "32" "38" "39" "48" ...
## $ Trans52 : chr [1:3] "39" "48" "68"
## $ Trans53 : chr [1:10] "38" "39" "48" "95" ...
## $ Trans54 : chr [1:16] "39" "41" "48" "212" ...
## $ Trans55 : chr [1:3] "300" "301" "302"
## $ Trans56 : chr [1:23] "36" "38" "39" "105" ...
## $ Trans57 : chr [1:7] "10" "322" "323" "324" ...
## $ Trans58 : chr [1:5] "39" "48" "152" "161" ...
## $ Trans59 : chr [1:3] "39" "329" "330"
## $ Trans60 : chr [1:10] "48" "331" "332" "333" ...
## $ Trans61 : chr [1:14] "18" "37" "38" "41" ...
## $ Trans62 : chr [1:7] "32" "39" "41" "48" ...
## $ Trans63 : chr [1:15] "48" "351" "352" "353" ...
## $ Trans64 : chr [1:2] "365" "366"
## $ Trans65 : chr [1:14] "38" "39" "41" "48" ...
## $ Trans66 : chr [1:17] "1" "11" "39" "41" ...
## $ Trans67 : chr [1:4] "386" "387" "388" "389"
## $ Trans68 : chr [1:3] "38" "41" "390"
## $ Trans69 : chr [1:3] "38" "55" "391"
## $ Trans70 : chr [1:15] "32" "43" "151" "152" ...
## $ Trans71 : chr [1:6] "338" "400" "401" "402" ...
## $ Trans72 : chr [1:4] "39" "405" "406" "407"
## $ Trans73 : chr [1:22] "48" "89" "101" "179" ...
## $ Trans74 : chr [1:8] "39" "45" "48" "248" ...
## $ Trans75 : chr [1:7] "141" "344" "427" "428" ...
## $ Trans76 : chr [1:4] "39" "432" "433" "434"
## $ Trans77 : chr [1:7] "39" "48" "65" "435" ...
## $ Trans78 : chr [1:23] "15" "23" "36" "38" ...
## $ Trans79 : chr [1:11] "48" "451" "452" "453" ...
## $ Trans80 : chr [1:16] "37" "38" "48" "147" ...
## $ Trans81 : chr [1:6] "39" "48" "472" "473" ...
## $ Trans82 : chr [1:3] "39" "41" "476"
## $ Trans83 : chr [1:3] "477" "478" "479"
## $ Trans84 : chr [1:9] "39" "161" "480" "481" ...
## $ Trans85 : chr [1:7] "32" "39" "41" "48" ...
## $ Trans86 : chr [1:6] "38" "39" "41" "105" ...
## $ Trans87 : chr [1:2] "60" "381"
## $ Trans88 : chr [1:17] "11" "39" "48" "255" ...
## $ Trans89 : chr "39"
## $ Trans90 : chr [1:3] "41" "110" "501"
## $ Trans91 : chr [1:8] "32" "38" "39" "48" ...
## $ Trans92 : chr [1:3] "38" "41" "504"
## $ Trans93 : chr [1:14] "225" "232" "347" "505" ...
## $ Trans94 : chr [1:9] "38" "39" "41" "48" ...
## $ Trans95 : chr [1:2] "39" "48"
## $ Trans96 : chr [1:4] "38" "39" "281" "517"
## $ Trans97 : chr [1:4] "2" "518" "519" "520"
## $ Trans98 : chr [1:3] "310" "521" "522"
## $ Trans99 : chr [1:3] "41" "523" "524"
## [list output truncated]some(retail.list)## $Trans12435
## [1] "48" "179" "438" "2942" "5829" "6068" "6698" "6794"
##
## $Trans14761
## [1] "39" "48" "155" "515" "577" "589" "789" "987" "1235" "1782"
## [11] "2315" "2389" "2408" "2449" "3316" "4349" "5116" "7869"
##
## $Trans19349
## [1] "39" "48" "2469"
##
## $Trans25154
## [1] "310" "1262" "1498" "1517"
##
## $Trans26013
## [1] "32" "79" "653"
##
## $Trans44741
## [1] "48" "225" "1763" "6515" "7756" "9235" "10447"
##
## $Trans61584
## [1] "39" "1081" "2388" "4676"
##
## $Trans61735
## [1] "249" "344" "424" "824" "896" "1277" "1445" "1973"
## [9] "2167" "4267" "4292" "5342" "5776" "10184" "10443" "10862"
## [17] "15076"
##
## $Trans78609
## [1] "32" "38" "39" "41" "55" "108" "548" "1180" "3015" "4841"
##
## $Trans78742
## [1] "39" "2689"rm(retail.raw)- 연관규칙분석을 위해 as(…, “transactions”)함수로 리스트를 transactions으로 변환시켜야함
retail.trans <- as(retail.list, "transactions")
summary(retail.trans)## transactions as itemMatrix in sparse format with
## 88162 rows (elements/itemsets/transactions) and
## 16470 columns (items) and a density of 0.0006257289
##
## most frequent items:
## 39 48 38 32 41 (Other)
## 50675 42135 15596 15167 14945 770058
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 3016 5516 6919 7210 6814 6163 5746 5143 4660 4086 3751 3285 2866 2620 2310
## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
## 2115 1874 1645 1469 1290 1205 981 887 819 684 586 582 472 480 355
## 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
## 310 303 272 234 194 136 153 123 115 112 76 66 71 60 50
## 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 44 37 37 33 22 24 21 21 10 11 10 9 11 4 9
## 61 62 63 64 65 66 67 68 71 73 74 76
## 7 4 5 2 2 5 3 3 1 1 1 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.00 8.00 10.31 14.00 76.00
##
## includes extended item information - examples:
## labels
## 1 0
## 2 1
## 3 10
##
## includes extended transaction information - examples:
## transactionID
## 1 Trans1
## 2 Trans2
## 3 Trans3rm(retail.list)2.3 Finding and Visualizing Association Rules
- apriori(data, parameters=…) 함수로 연관규칙 탐색
- minimum support = 0.001 and minimum confidence = 0.4
- 해당 신뢰도 지지도에서 5,944 rules 찾음
retail.rules <- apriori(retail.trans, parameter=list(supp=0.001, conf=0.4))##
## Parameter specification:
## confidence minval smax arem aval originalSupport support minlen maxlen
## 0.4 0.1 1 none FALSE TRUE 0.001 1 10
## target ext
## rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## apriori - find association rules with the apriori algorithm
## version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[16470 item(s), 88162 transaction(s)] done [0.49s].
## sorting and recoding items ... [2117 item(s)] done [0.05s].
## creating transaction tree ... done [0.16s].
## checking subsets of size 1 2 3 4 5 6 done [0.45s].
## writing ... [5944 rule(s)] done [0.03s].
## creating S4 object ... done [0.04s].- 연관규칙 시각화
- upper left with high lift인 몇개 규칙발견
library(arulesViz)
plot(retail.rules)
- interactive=TRUE 인수로 다시 시각화
- 인터랙티브 모드에서는 일부지역의 룰을 조사할수 있다.
- 플랏 창에 관심있는 영역의 코너를 클릭하고 다시 반대편 코너를 클릭
- 해당영역의 규칙 리스트를 볼수있다. 끝낼때는 end클릭
#plot(retail.rules, interactive=TRUE)3.1 Finding and Plotting Subsets of Rules
- 일반적인 장바구니 분석의 목적은 높은 리프트의 규칙을 찾는것
- lift값을 sorting해서 찾을수 있음 lift가 가장 높은 50개 규칙
- 첫번째 두번째 룰은 같은 룰이다(696-699)/ 지지도는 약간다름
retail.hi <- head(sort(retail.rules, by="lift"), 50)
inspect(retail.hi)## lhs rhs support confidence lift
## 99 {696} => {699} 0.001032191 0.5833333 338.3410
## 98 {699} => {696} 0.001032191 0.5986842 338.3410
## 5097 {1818,3311,795} => {1819} 0.001088905 0.9056604 318.1069
## 193 {3402} => {3535} 0.001417844 0.7062147 305.2024
## 194 {3535} => {3402} 0.001417844 0.6127451 305.2024
## 5099 {1818,1819,795} => {3311} 0.001088905 0.8275862 302.7455
## 5100 {1819,3311,795} => {1818} 0.001088905 0.7741935 302.0108
## 2856 {3311,795} => {1819} 0.001406502 0.8435374 296.2866
## 5098 {1818,1819,3311} => {795} 0.001088905 0.8421053 295.7836
## 2481 {3537,39} => {3535} 0.001043533 0.6764706 292.3480
## 2482 {3535,39} => {3537} 0.001043533 0.6571429 288.2340
## 2833 {1818,1819} => {3311} 0.001293074 0.7862069 287.6082
## 2832 {1818,3311} => {1819} 0.001293074 0.8142857 286.0122
## 2831 {3311,795} => {1818} 0.001202332 0.7210884 281.2947
## 2839 {1818,1819} => {795} 0.001315760 0.8000000 280.9944
## 453 {3537} => {3535} 0.001474558 0.6467662 279.5108
## 454 {3535} => {3537} 0.001474558 0.6372549 279.5108
## 2838 {1818,795} => {1819} 0.001315760 0.7733333 271.6279
## 2858 {1819,795} => {3311} 0.001406502 0.7425150 271.6249
## 2840 {1819,795} => {1818} 0.001315760 0.6946108 270.9658
## 2866 {3311,39} => {1819} 0.001304417 0.7615894 267.5030
## 2829 {1818,3311} => {795} 0.001202332 0.7571429 265.9411
## 191 {3402} => {3537} 0.001213675 0.6045198 265.1526
## 192 {3537} => {3402} 0.001213675 0.5323383 265.1526
## 1211 {1819} => {3311} 0.002041696 0.7171315 262.3392
## 1210 {3311} => {1819} 0.002041696 0.7468880 262.3392
## 2830 {1818,795} => {3311} 0.001202332 0.7066667 258.5110
## 2867 {1819,39} => {3311} 0.001304417 0.7055215 258.0920
## 3360 {1379,309} => {1378} 0.001327102 0.7048193 254.6651
## 3356 {1080,1378} => {1379} 0.001077562 0.8119658 252.9489
## 2863 {3311,48} => {1819} 0.001213675 0.7181208 252.2349
## 3351 {1269,1379} => {1378} 0.001009505 0.6953125 251.2301
## 2864 {1819,48} => {3311} 0.001213675 0.6858974 250.9132
## 2834 {1819,3311} => {1818} 0.001293074 0.6333333 247.0617
## 3047 {1080,309} => {1380} 0.001043533 0.5786164 244.0764
## 3357 {1080,1379} => {1378} 0.001077562 0.6737589 243.4423
## 2998 {1819,48} => {795} 0.001225018 0.6923077 243.1683
## 3000 {39,795} => {1819} 0.001225018 0.6923077 243.1683
## 2857 {1819,3311} => {795} 0.001406502 0.6888889 241.9674
## 3359 {1378,309} => {1379} 0.001327102 0.7748344 241.3815
## 2997 {48,795} => {1819} 0.001225018 0.6835443 240.0902
## 2837 {3311,39} => {1818} 0.001043533 0.6092715 237.6752
## 2846 {39,795} => {1818} 0.001077562 0.6089744 237.5593
## 3042 {1379,1380} => {309} 0.001043533 0.8214286 234.3650
## 3044 {1379,309} => {1380} 0.001043533 0.5542169 233.7841
## 1354 {795} => {1819} 0.001894240 0.6653386 233.6956
## 1355 {1819} => {795} 0.001894240 0.6653386 233.6956
## 1199 {795} => {1818} 0.001701413 0.5976096 233.1259
## 1198 {1818} => {795} 0.001701413 0.6637168 233.1259
## 3001 {1819,39} => {795} 0.001225018 0.6625767 232.7254- 그래프는 높은수준의 발생패턴을 파악하는데 유용함
- 리프트 상위 50개 규칙에 대한 차트 트 plot(…, method=“graph”)
- 원의 크기는 지지도에 비례한다.
- 음영은 리프트값에 비례한다.(진할수록 높음)
- 다음 아이템들의 군집을 확인하수 있다.
- {3402, 3535, 3537},
- {309, 1080, 1269, 1378, 1379, 1380}
- 39번 아이템은 두 군집과 연관된 아이템이다.
- 화살표방향은 레프트핸드->라이트핸드
plot(retail.hi, method="graph", control=list(type="items"))
3.2 Using Profit Margin Data with Transactions: An Initial Start
- 분석가라면 제품의 마진이나 고개의 특성에 관심을 가질것
- 현재 데이터에는 가격이나 마진에 대한 내용이 없음
- 아이템별 가격을 넣어보자자
- 현재 아이템명은 수치형 자료가 아닌 문자이다.
retail.itemnames <- sort(unique(unlist(as(retail.trans, "list"))))
head(retail.itemnames); tail(retail.itemnames)## [1] "0" "1" "10" "100" "1000" "10000"## [1] "9994" "9995" "9996" "9997" "9998" "9999"- 아이템별 가격과 마진에대한 데이터를 nrorm으로만들자
set.seed(03870)
retail.margin <- data.frame(margin=rnorm(length(retail.itemnames),
mean=0.30,
sd=0.30))
quantile(retail.margin$margin)## 0% 25% 50% 75% 100%
## -1.1090452 0.1045897 0.3026245 0.5050533 1.5542344- 아이템 이름에 마진을 매칭시켜서 넣자
rownames(retail.margin) <- retail.itemnames
head(retail.margin); tail(retail.margin)## margin
## 0 0.88340359
## 1 0.52964087
## 10 0.22114807
## 100 -0.03793804
## 1000 0.35830549
## 10000 0.37334289## margin
## 9994 0.3931183
## 9995 0.1459387
## 9996 0.5623275
## 9997 0.7698083
## 9998 -0.4374318
## 9999 0.6850124library(car); some(retail.margin)## margin
## 12336 0.18504274
## 13678 0.47677608
## 14641 0.70268882
## 1506 0.68192909
## 15718 0.53122274
## 1683 0.35093588
## 7104 -0.09544371
## 7692 0.82340505
## 7700 0.11612354
## 8718 0.57274873- 재품명으로 마진값을 추출
- 장바구니 상품들의 마진을 합할수 있음 {39, 48}
- 39, 48 장바구니 의 마진 합은 -0.09
retail.margin[c("39", "48"), ]## [1] 0.1217833 -0.2125105sum(retail.margin[c("39", "48"), ])## [1] -0.09072725- transaction을 다시 리스트로 변환 list함수
(basket.items <- as(retail.trans[3], "list")[[1]])## [1] "33" "34" "35"retail.margin[basket.items, ]## [1] 0.3817115 0.6131403 0.1979879sum(retail.margin[basket.items, ])## [1] 1.192843.3 Language Brief: A Function for Margin Using an Object’s class
- 마진을 산출하는 함수 작성
- 깊이있는 프로그래밍을 원치 않으면 스킵할것
4. Rules in Non-Transactional Data: Exploring
- “transaction”은 데이터가 동시발한 지점을 말한다.
- 이는 연관규칙이 다른 일반 데이터프레임에도 적용가능함을 의미한다.
- 고객세그먼트 탐색에 연관규칙분석을 적용해보자
seg.df <- read.csv("http://goo.gl/qw303p")
summary(seg.df)## age gender income kids ownHome
## Min. :19.26 Female:157 Min. : -5183 Min. :0.00 ownNo :159
## 1st Qu.:33.01 Male :143 1st Qu.: 39656 1st Qu.:0.00 ownYes:141
## Median :39.49 Median : 52014 Median :1.00
## Mean :41.20 Mean : 50937 Mean :1.27
## 3rd Qu.:47.90 3rd Qu.: 61403 3rd Qu.:2.00
## Max. :80.49 Max. :114278 Max. :7.00
## subscribe Segment
## subNo :260 Moving up : 70
## subYes: 40 Suburb mix:100
## Travelers : 80
## Urban hip : 50
##
## 4.1 Language Brief: Slicing Continuous Data with cut()
- 명목형자료이어야 연관규칙을 적용할수 있음
- 3개 연속형 자료(나이, 소득, 자녀수)를 명목형으로 변환
seg.fac <- seg.df
seg.fac$age <- cut(seg.fac$age,
breaks=c(0,25,35,55,65,100),
labels=c("19-24", "25-34", "35-54", "55-64", "65+"),
right=FALSE, ordered_result=TRUE)
summary(seg.fac$age)## 19-24 25-34 35-54 55-64 65+
## 38 58 152 38 14seg.fac$income <- cut(seg.fac$income,
breaks=c(-100000, 40000, 70000, 1000000),
labels=c("Low", "Medium", "High"),
right=FALSE,
ordered_result=TRUE)
seg.fac$kids <- cut(seg.fac$kids,
breaks=c(0, 1, 2, 3, 100),
labels=c("No kids", "1 kid", "2 kids", "3+ kids"),
right=FALSE,
ordered_result=TRUE)
summary(seg.fac)## age gender income kids ownHome
## 19-24: 38 Female:157 Low : 77 No kids:121 ownNo :159
## 25-34: 58 Male :143 Medium:183 1 kid : 70 ownYes:141
## 35-54:152 High : 40 2 kids : 51
## 55-64: 38 3+ kids: 58
## 65+ : 14
## subscribe Segment
## subNo :260 Moving up : 70
## subYes: 40 Suburb mix:100
## Travelers : 80
## Urban hip : 50
## 4.2 Exploring Segment Associations
- 데이터 형식 변환 using as(…, “transactions”)
library(arules)
library(arulesViz)
seg.trans <- as(seg.fac, "transactions")
summary(seg.trans)## transactions as itemMatrix in sparse format with
## 300 rows (elements/itemsets/transactions) and
## 22 columns (items) and a density of 0.3181818
##
## most frequent items:
## subscribe=subNo income=Medium ownHome=ownNo gender=Female
## 260 183 159 157
## age=35-54 (Other)
## 152 1189
##
## element (itemset/transaction) length distribution:
## sizes
## 7
## 300
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7 7 7 7 7 7
##
## includes extended item information - examples:
## labels variables levels
## 1 age=19-24 age 19-24
## 2 age=25-34 age 25-34
## 3 age=35-54 age 35-54
##
## includes extended transaction information - examples:
## transactionID
## 1 1
## 2 2
## 3 3- 연관규칙분석
seg.rules <- apriori(seg.trans,
parameter=list(support=0.1, conf=0.4,target="rules"))##
## Parameter specification:
## confidence minval smax arem aval originalSupport support minlen maxlen
## 0.4 0.1 1 none FALSE TRUE 0.1 1 10
## target ext
## rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## apriori - find association rules with the apriori algorithm
## version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[22 item(s), 300 transaction(s)] done [0.00s].
## sorting and recoding items ... [21 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [579 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(seg.rules)## set of 579 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4 5
## 8 109 263 174 25
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 3.000 3.171 4.000 5.000
##
## summary of quality measures:
## support confidence lift
## Min. :0.1000 Min. :0.4026 Min. :0.7941
## 1st Qu.:0.1100 1st Qu.:0.5200 1st Qu.:1.0000
## Median :0.1300 Median :0.6522 Median :1.1002
## Mean :0.1632 Mean :0.6847 Mean :1.4715
## 3rd Qu.:0.1867 3rd Qu.:0.8421 3rd Qu.:1.4896
## Max. :0.8667 Max. :1.0000 Max. :6.0000
##
## mining info:
## data ntransactions support confidence
## seg.trans 300 0.1 0.4- 결과시각화
plot(seg.rules)
#plot(seg.rules, interactive=T)- 상위 30개 규칙 확인
seg.hi <- head(sort(seg.rules, by="lift"), 35)
inspect(seg.hi)## lhs rhs support confidence lift
## 1 {age=19-24} => {Segment=Urban hip} 0.1266667 1.0000000 6.000000
## 2 {Segment=Urban hip} => {age=19-24} 0.1266667 0.7600000 6.000000
## 3 {age=19-24,
## income=Low} => {Segment=Urban hip} 0.1266667 1.0000000 6.000000
## 4 {income=Low,
## Segment=Urban hip} => {age=19-24} 0.1266667 0.7600000 6.000000
## 5 {age=19-24,
## ownHome=ownNo} => {Segment=Urban hip} 0.1000000 1.0000000 6.000000
## 6 {age=19-24,
## subscribe=subNo} => {Segment=Urban hip} 0.1000000 1.0000000 6.000000
## 7 {age=19-24,
## income=Low,
## ownHome=ownNo} => {Segment=Urban hip} 0.1000000 1.0000000 6.000000
## 8 {age=19-24,
## income=Low,
## subscribe=subNo} => {Segment=Urban hip} 0.1000000 1.0000000 6.000000
## 9 {ownHome=ownNo,
## Segment=Urban hip} => {age=19-24} 0.1000000 0.7500000 5.921053
## 10 {subscribe=subNo,
## Segment=Urban hip} => {age=19-24} 0.1000000 0.7500000 5.921053
## 11 {income=Low,
## ownHome=ownNo,
## Segment=Urban hip} => {age=19-24} 0.1000000 0.7500000 5.921053
## 12 {income=Low,
## subscribe=subNo,
## Segment=Urban hip} => {age=19-24} 0.1000000 0.7500000 5.921053
## 13 {income=Low,
## ownHome=ownNo} => {Segment=Urban hip} 0.1333333 0.7843137 4.705882
## 14 {income=Low,
## ownHome=ownNo,
## subscribe=subNo} => {Segment=Urban hip} 0.1066667 0.7804878 4.682927
## 15 {income=Low,
## ownHome=ownNo} => {age=19-24} 0.1000000 0.5882353 4.643963
## 16 {gender=Male,
## income=Low} => {Segment=Urban hip} 0.1000000 0.6818182 4.090909
## 17 {income=Low} => {Segment=Urban hip} 0.1666667 0.6493506 3.896104
## 18 {age=19-24} => {income=Low} 0.1266667 1.0000000 3.896104
## 19 {income=Low} => {age=19-24} 0.1266667 0.4935065 3.896104
## 20 {Segment=Urban hip} => {income=Low} 0.1666667 1.0000000 3.896104
## 21 {age=19-24,
## Segment=Urban hip} => {income=Low} 0.1266667 1.0000000 3.896104
## 22 {age=19-24,
## ownHome=ownNo} => {income=Low} 0.1000000 1.0000000 3.896104
## 23 {age=19-24,
## subscribe=subNo} => {income=Low} 0.1000000 1.0000000 3.896104
## 24 {gender=Male,
## Segment=Urban hip} => {income=Low} 0.1000000 1.0000000 3.896104
## 25 {ownHome=ownNo,
## Segment=Urban hip} => {income=Low} 0.1333333 1.0000000 3.896104
## 26 {subscribe=subNo,
## Segment=Urban hip} => {income=Low} 0.1333333 1.0000000 3.896104
## 27 {age=19-24,
## ownHome=ownNo,
## Segment=Urban hip} => {income=Low} 0.1000000 1.0000000 3.896104
## 28 {age=19-24,
## subscribe=subNo,
## Segment=Urban hip} => {income=Low} 0.1000000 1.0000000 3.896104
## 29 {ownHome=ownNo,
## subscribe=subNo,
## Segment=Urban hip} => {income=Low} 0.1066667 1.0000000 3.896104
## 30 {age=55-64} => {Segment=Travelers} 0.1266667 1.0000000 3.750000
## 31 {Segment=Travelers} => {age=55-64} 0.1266667 0.4750000 3.750000
## 32 {age=55-64,
## kids=No kids} => {Segment=Travelers} 0.1266667 1.0000000 3.750000
## 33 {kids=No kids,
## Segment=Travelers} => {age=55-64} 0.1266667 0.4750000 3.750000
## 34 {age=55-64,
## subscribe=subNo} => {Segment=Travelers} 0.1100000 1.0000000 3.750000
## 35 {age=55-64,
## kids=No kids,
## subscribe=subNo} => {Segment=Travelers} 0.1100000 1.0000000 3.750000- 연관규칙 시각화
plot(seg.hi, method="graph", control=list(type="items"))
- 리프트 상위35개 이후의 연관규칙 시각화
seg.next <- sort(seg.rules, by="lift")[36:60]
plot(seg.next, method="graph", control=list(type="items"))