菜籃分析 (Basket Analysis)
Tony Chuo 2017/11/18
Association Rules for Market Basket Analysis (R) cited & modified from: http://www.informit.com/promotions/code-files-modeling-techniques-in-predictive-analytics-141177
library(arules) # association rules
library(arulesViz) # data visualization of association rules
library(RColorBrewer) # color palettes for plots
data(Groceries) # grocery transactions object from arules package
dim(Groceries) # 9835 baskets x 169 items ## [1] 9835 169inspect(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}# as(Groceries, "data.frame") Examine frequency for each item with support greater than 0.025
par(cex=0.8)
itemFrequencyPlot(Groceries, support = 0.025, xlim = c(0,0.3),
type = "relative", horiz = TRUE, col = "dark red", las = 1,
xlab = paste0(
"Proportion of Market Baskets Containing Item\n",
"(Item Relative Frequency or Support)"))
Explore possibilities for combining similar items
df = itemInfo(Groceries)
str(df) # levels 10, 55## 'data.frame': 169 obs. of 3 variables:
## $ labels: chr "frankfurter" "sausage" "liver loaf" "ham" ...
## $ level2: Factor w/ 55 levels "baby food","bags",..: 44 44 44 44 44 44 44 42 42 41 ...
## $ level1: Factor w/ 10 levels "canned food",..: 6 6 6 6 6 6 6 6 6 6 ...Aggregate items using the 55 level2 levels for food categories
groceries <- aggregate(Groceries, itemInfo(Groceries)[["level2"]])
# dim(groceries) # 9835 baskets x 55 items
itemFrequencyPlot(groceries, support = 0.025, xlim = c(0,0.5),
type = "relative", horiz = TRUE, col = "blue", las = 1,
xlab = paste0("Proportion of Market Baskets Containing Item\n",
"(Item Relative Frequency or Support)"))
Obtain large set of association rules for items by category and all shoppers
This is done by setting very low criteria for support and confidence:
first.rules <- apriori(groceries,
parameter = list(support = 0.001, confidence = 0.05))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.05 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 ...[55 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [54 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 done [0.02s].
## writing ... [69921 rule(s)] done [0.01s].
## creating S4 object ... done [0.03s].summary(first.rules) # yields 69,921 rules... too many## set of 69921 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4 5 6 7 8
## 21 1205 10467 23895 22560 9888 1813 72
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.000 4.502 5.000 8.000
##
## summary of quality measures:
## support confidence lift count
## Min. :0.001017 Min. :0.0500 Min. : 0.4475 Min. : 10.00
## 1st Qu.:0.001118 1st Qu.:0.2110 1st Qu.: 1.8315 1st Qu.: 11.00
## Median :0.001525 Median :0.4231 Median : 2.2573 Median : 15.00
## Mean :0.002488 Mean :0.4364 Mean : 2.5382 Mean : 24.47
## 3rd Qu.:0.002339 3rd Qu.:0.6269 3rd Qu.: 2.9662 3rd Qu.: 23.00
## Max. :0.443010 Max. :1.0000 Max. :16.1760 Max. :4357.00
##
## mining info:
## data ntransactions support confidence
## groceries 9835 0.001 0.05Select association rules using thresholds for support and confidence
second.rules <- apriori(groceries,
parameter = list(support = 0.025, confidence = 0.05))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.05 0.1 1 none FALSE TRUE 5 0.025 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: 245
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[55 item(s), 9835 transaction(s)] done [0.02s].
## sorting and recoding items ... [32 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [344 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(second.rules) # yields 344 rules## set of 344 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4
## 21 162 129 32
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 2.0 2.0 2.5 3.0 4.0
##
## summary of quality measures:
## support confidence lift count
## Min. :0.02542 Min. :0.05043 Min. :0.6669 Min. : 250.0
## 1st Qu.:0.03030 1st Qu.:0.18202 1st Qu.:1.2498 1st Qu.: 298.0
## Median :0.03854 Median :0.39522 Median :1.4770 Median : 379.0
## Mean :0.05276 Mean :0.37658 Mean :1.4831 Mean : 518.9
## 3rd Qu.:0.05236 3rd Qu.:0.51271 3rd Qu.:1.7094 3rd Qu.: 515.0
## Max. :0.44301 Max. :0.79841 Max. :2.4073 Max. :4357.0
##
## mining info:
## data ntransactions support confidence
## groceries 9835 0.025 0.05Data visualization of association rules in scatter plot
plot(second.rules, cex=0.8,
control=list(jitter=2, col = rev(brewer.pal(9, "RdYlGn"))),
shading = "lift") 
Grouped matrix of rules
plot(second.rules, method="grouped",
control=list(col = rev(brewer.pal(9, "RdYlGn"))))
Select rules with vegetables in consequent (right-hand-side) item subsets
vegie.rules <- subset(second.rules, subset = rhs %pin% "vegetables")
inspect(vegie.rules) # 41 rules## lhs rhs support confidence lift count
## [1] {} => {vegetables} 0.27300458 0.2730046 1.0000000 2685
## [2] {poultry} => {vegetables} 0.02897814 0.5745968 2.1047148 285
## [3] {pork} => {vegetables} 0.03009659 0.5220459 1.9122238 296
## [4] {staple foods} => {vegetables} 0.02613116 0.5160643 1.8903136 257
## [5] {eggs} => {vegetables} 0.03141840 0.4951923 1.8138608 309
## [6] {games/books/hobby} => {vegetables} 0.02785968 0.3145809 1.1522918 274
## [7] {long-life bakery products} => {vegetables} 0.02907982 0.3492063 1.2791227 286
## [8] {perfumery} => {vegetables} 0.03213015 0.4056483 1.4858662 316
## [9] {beef} => {vegetables} 0.04585663 0.5595533 2.0496116 451
## [10] {bags} => {vegetables} 0.03141840 0.3175745 1.1632571 309
## [11] {vinegar/oils} => {vegetables} 0.04199288 0.4666667 1.7093731 413
## [12] {chocolate} => {vegetables} 0.03192679 0.2934579 1.0749195 314
## [13] {beer} => {vegetables} 0.03406202 0.2189542 0.8020168 335
## [14] {frozen foods} => {vegetables} 0.04738180 0.4052174 1.4842879 466
## [15] {cheese} => {vegetables} 0.05531266 0.4365971 1.5992300 544
## [16] {sausage} => {vegetables} 0.07625826 0.4032258 1.4769929 750
## [17] {fruit} => {vegetables} 0.10706660 0.4297959 1.5743176 1053
## [18] {non-alc. drinks} => {vegetables} 0.09456024 0.2974097 1.0893944 930
## [19] {bread and backed goods} => {vegetables} 0.11621759 0.3363743 1.2321198 1143
## [20] {dairy produce} => {vegetables} 0.17041179 0.3846683 1.4090180 1676
## [21] {beef,
## dairy produce} => {vegetables} 0.02989324 0.6074380 2.2250104 294
## [22] {dairy produce,
## vinegar/oils} => {vegetables} 0.03141840 0.5355286 1.9616103 309
## [23] {dairy produce,
## frozen foods} => {vegetables} 0.03436706 0.5121212 1.8758704 338
## [24] {cheese,
## fruit} => {vegetables} 0.02674123 0.5197628 1.9038613 263
## [25] {bread and backed goods,
## cheese} => {vegetables} 0.02887646 0.4536741 1.6617821 284
## [26] {cheese,
## dairy produce} => {vegetables} 0.04219624 0.4987981 1.8270686 415
## [27] {fruit,
## sausage} => {vegetables} 0.03426538 0.5290424 1.9378517 337
## [28] {non-alc. drinks,
## sausage} => {vegetables} 0.03029995 0.4156206 1.5223944 298
## [29] {bread and backed goods,
## sausage} => {vegetables} 0.04382308 0.4229637 1.5492916 431
## [30] {dairy produce,
## sausage} => {vegetables} 0.05266904 0.4905303 1.7967842 518
## [31] {fruit,
## non-alc. drinks} => {vegetables} 0.04361973 0.4657980 1.7061914 429
## [32] {bread and backed goods,
## fruit} => {vegetables} 0.05124555 0.4763705 1.7449177 504
## [33] {dairy produce,
## fruit} => {vegetables} 0.07869853 0.5032510 1.8433793 774
## [34] {bread and backed goods,
## non-alc. drinks} => {vegetables} 0.04636502 0.3731588 1.3668590 456
## [35] {dairy produce,
## non-alc. drinks} => {vegetables} 0.06446365 0.4243641 1.5544213 634
## [36] {bread and backed goods,
## dairy produce} => {vegetables} 0.08195221 0.4366197 1.5993128 806
## [37] {dairy produce,
## fruit,
## sausage} => {vegetables} 0.02714794 0.5741935 2.1032378 267
## [38] {bread and backed goods,
## dairy produce,
## sausage} => {vegetables} 0.03284189 0.5135135 1.8809704 323
## [39] {dairy produce,
## fruit,
## non-alc. drinks} => {vegetables} 0.03304525 0.5183413 1.8986543 325
## [40] {bread and backed goods,
## dairy produce,
## fruit} => {vegetables} 0.04077275 0.5276316 1.9326840 401
## [41] {bread and backed goods,
## dairy produce,
## non-alc. drinks} => {vegetables} 0.03345196 0.4627286 1.6949480 329Sort by lift and identify the top 10 rules
top.vegie.rules <- head(sort(vegie.rules, decreasing = TRUE, by = "lift"), 10)
inspect(top.vegie.rules) ## lhs rhs support confidence lift count
## [1] {beef,
## dairy produce} => {vegetables} 0.02989324 0.6074380 2.225010 294
## [2] {poultry} => {vegetables} 0.02897814 0.5745968 2.104715 285
## [3] {dairy produce,
## fruit,
## sausage} => {vegetables} 0.02714794 0.5741935 2.103238 267
## [4] {beef} => {vegetables} 0.04585663 0.5595533 2.049612 451
## [5] {dairy produce,
## vinegar/oils} => {vegetables} 0.03141840 0.5355286 1.961610 309
## [6] {fruit,
## sausage} => {vegetables} 0.03426538 0.5290424 1.937852 337
## [7] {bread and backed goods,
## dairy produce,
## fruit} => {vegetables} 0.04077275 0.5276316 1.932684 401
## [8] {pork} => {vegetables} 0.03009659 0.5220459 1.912224 296
## [9] {cheese,
## fruit} => {vegetables} 0.02674123 0.5197628 1.903861 263
## [10] {dairy produce,
## fruit,
## non-alc. drinks} => {vegetables} 0.03304525 0.5183413 1.898654 325plot(top.vegie.rules, method="graph",
control=list(type="items", alpha=1, labelCol="blue"),
shading = "lift")## Warning: Unknown control parameters: type## Available control parameters (with default values):
## main = Graph for 10 rules
## nodeColors = c("#66CC66FF", "#9999CCFF")
## 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
Practice:
Suppose your client is someone other than the local farmer,
- a meat producer/butcher, dairy, or brewer perhaps.
- Determine association rules relevant to your client’s products
- guided by the market basket model. What recommendations
- would you make about future marketplace actions?