R for Marketing Research and Analytics
Chris Chapman and Elea McDonnell Feit
February 2016
Chapter 12: Association Rules
Website for all data files:
http://r-marketing.r-forge.r-project.org/data.html
Brief introduction
Association rule mining looks for associations among events and data points, especially in data where absolute frequencies are low.
Some examples include:
- Associations between items sold (where any given transaction usually purchases 0 of any specific item)
- Associations between individuals and items
- Associations between demographic characteristics and other behaviors such as purchase
- Associations between interventions (where there is a large set) and behaviors
- Recommendation engines deriving recommendations from any of the above
In shopping contexts, this is often known as market basket analysis.
Fundamental concepts
A transaction is a set of discrete data points (e.g., an item sold) that go together. For example, a transaction might be {hot dog, mustard, relish, cola}.
A rule is the conditional relationship of item sets. E.g.,: {hog dog, mustard} \( \rightarrow \) {relish, cola}. It only implies relationship, not causation.
The goal is to find rules where the association is higher than predicted by independent frequencies … in other words, lift. This involves:
support(SET): the proportion of transactions that contain SET. For example, support(hot dog, relish)=0.05.
confidence(RULE): the proportion of times that items on the right hand side of a rule occur, relative to the left hand side. For example: confidence(relish \( \rightarrow \) hot dog)=0.50. {hot dog} appears in 50% of the transactions where {relish} appears.
Lift
lift(RULE) is the support for a joint RULE relative to the multiplied support of its parts taken separately. For example, suppose:
- support(relish) = 0.01 (relish sold in 1% of transactions)
- support(hot dog) = 0.01 (hot dog sold in 1% of transactions)
- support(relish, hot dog) = 0.005 (relish + hot dog sold in 0.5% of transactions)
Then:
- lift(relish \( \rightarrow \) hot dog) = 0.005 / (0.01 * 0.01) = 50
This says the odds of seeing a transaction containg a hot dog alongside relish is 50x greater that one would expect if the two items were independent.
Grocery example
Data at a category level for 169 categories and 9835 transactions.
# install.packages(c("arules", "arulesViz"))
library(arules)
data("Groceries") # data included with arules package
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 sausage
The structure of transactions
inspect(head(Groceries))
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}
Finding rules
The apriori algorithm (see the book) is implemented in very fast C++ code, and searches for rules with specified levels of support and confidence.
groc.rules <- apriori(Groceries, parameter=list(supp=0.01, conf=0.3,
target="rules"))
Apriori
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
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 [0.00s].
checking subsets of size 1 2 3 4 done [0.00s].
writing ... [125 rule(s)] done [0.00s].
creating S4 object ... done [0.00s].
Inspecting the rules
Use inspect() to show the actual rules, filtering as desired:
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 3.144780
For example, if a transaction has beef, it is 3x more likely to have root vegetables (onions?) than expected from independent frequencies.
A larger data set
Brijs et al (1999) have provided real data from a supermarket chain (item labels replaced by sequential numbers).
Get the raw data:
retail.raw <- readLines("http://goo.gl/FfjDAO") # takes a while
summary(retail.raw)
Length Class Mode
88162 character character
head(retail.raw, 5)
[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 "
tail(retail.raw, 3)
[1] "2310 4267 " "39 48 2528 " "32 39 205 242 1393 "
Convert lines to item sets
We split the lines at spaces, and number the resulting transaction vectors:
retail.list <- strsplit(retail.raw, " ")
names(retail.list) <- paste("Trans", 1:length(retail.list), sep="")
str(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]
Now convert to transaction sets
We convert those vectors to the transactions class from arules:
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 Trans3
rm(retail.raw, retail.list) # clean up memory
Find the association rules
We ask for rules where the items appear in at least 0.1% of transactions (support), and associations co-occur with conditional odds of 40% or more (confidence). This yields >5000 rules in these data.
retail.rules <- apriori(retail.trans,
parameter=list(supp=0.001, conf=0.4))
Apriori
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
Absolute minimum support count: 88
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[16470 item(s), 88162 transaction(s)] done [0.16s].
sorting and recoding items ... [2117 item(s)] done [0.02s].
creating transaction tree ... done [0.06s].
checking subsets of size 1 2 3 4 5 6 done [0.11s].
writing ... [5944 rule(s)] done [0.01s].
creating S4 object ... done [0.03s].
Visualizing Rules
A default visualization for rules shows confidence (association strength) vs. support (frequency of occurrence):
library(arulesViz) # install if needed
plot(retail.rules)
Interactive visualization
Try it yourself: add interactive=TRUE:
plot(retail.rules, interactive=TRUE)
This allows you to zoom into regions and click on rules to inspect them.
Rule subsets
Most commonly we would want to find top rules, e.g., by lift:
retail.hi <- head(sort(retail.rules, by="lift"), 50) # top 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
Graph plot
A graph plot shows a network with nodes comprising item sets. Each connection is a rule. For the top 50 rules by lift:
plot(retail.hi, method="graph", control=list(type="items"))
Margin, revenue, etc. by association
Assume that we have margin per item in retail.margin:
library(car)
some(retail.margin, 5)
margin
12336 0.18504274
13678 0.47677608
1506 0.68192909
1683 0.35093588
7104 -0.09544371
We can use vector indexing to find the margin for a basket (quantity 1):
retail.margin[c("39", "48", "1080"), ]
[1] 0.1217833 -0.2125105 0.3041887
sum(retail.margin[c("39", "48", "1080"), ])
[1] 0.2134614
Margin for a transaction
If we take a transaction and convert it to a list of items:
(basket.items <- as(retail.trans[33], "list")[[1]])
[1] "140" "187" "188" "36" "38" "41" "48"
… then we can find its margin:
round(retail.margin[basket.items, ], 2)
[1] 0.44 -0.06 -0.02 -0.07 0.51 -0.02 -0.21
sum(retail.margin[basket.items, ])
[1] 0.5550056
This allows us to score the association rules for margin. We could then segment those by other information such as respondent data.
For more info, including how to build out a more robust and realistic margin function, see the book, Section 12.3.3.
Non-basket data
Association rules can work with arbitrary data. One interesting use is to explore demographic & behavioral associations in segmentation-related data.
We'll get consumer segmentation data from Chapter 5 (subscription status with demographics):
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
Cutting data into factors
To make associations, we need nominal “items” to associate. For continuous
data, we might recode into factors using cut():
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)
seg.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
Rules in the segmentation data
The association rule process is exactly the same:
seg.trans <- as(seg.fac, "transactions") # code as transactions
seg.rules <- apriori(seg.trans, parameter=list(support=0.1, conf=0.4,
target="rules"))
Apriori
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
Absolute minimum support count: 30
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].
High-lift rules
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
Visualize high-lift rules
plot(seg.hi, method="graph", control=list(type="items"))
Discussion
Association rules are perhaps best viewed as an exploratory method. They generally do not express:
- Confidence intervals needed for inference
- Causation (although can be easily misunderstood as such)
Metrics such as lift are prone to overfitting in single samples. So, if you want to draw inferences from association rules, consider careful iteration and model testing with multiple samples (e.g., in-store test).
Meanwhile, they can be quite useful to generate hypotheses and to view data relationships in new ways.
Q&A and a break!
Notes
This presentation is based on Chapter 12 of Chapman and Feit, R for Marketing Research and Analytics © 2015 Springer.
All code in the presentation is licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\ Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Reference for supermarket data, used with permission:
Brijs, T., Swinnen, G., Vanhoof, K., & Wets, G. (1999). “Using association rules for product assortment decisions: A case study.” In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 254–260), Association for Computing Machinery.