Association Analysis
Anik Nur Habyba
11/10/2021
ASSOCIATION ANALYSIS
TUJUAN PEMBELAJARAN
Students can analyze, interpret data and information and make appropriate decisions based on the association analysis approach (CPMK1, CPMK2, KUE, KKB). - Affinity Analysis - Apriori Algorithm in R Studio - FP Growth in R Studio
##AFFINITY ANALYSIS Affinity analysis is the study of attributes or characteristics that “go together”. Methods for affinity analysis, also known as market basket analysis, seek to uncover associations among these attributes; that is, it seeks to uncover rules for quantifying the relationship between two or more attributes. Association rules take the form “If antecedent, then consequent”, along with a measure of the support and confidence associated with the rule.
###INSTALL PACKAGES Install packages arules in console -arules -arulesViz -grid
Lets consider the rule A => B in order to compute these metrics.
###EXAMPLE Transactions data Lets play with the Groceries data that comes with the arules pkg. Unlike dataframe, using head(Groceries) does not display the transaction items in the data. To view the transactions, use the inspect() function instead.
Since association mining deals with transactions, the data has to be converted to one of class transactions, made available in R through the arules pkg. This is a necessary step because the apriori() function accepts transactions data of class transactions only.
###LOAD DATA
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(arulesViz)
library(grid)
data(Groceries)
class(Groceries)## [1] "transactions"
## attr(,"package")
## [1] "arules"inspect(head(Groceries, 3))## items
## [1] {citrus fruit,
## semi-finished bread,
## margarine,
## ready soups}
## [2] {tropical fruit,
## yogurt,
## coffee}
## [3] {whole milk}How to see the most frequent items?
The eclat() takes in a transactions object and gives the most frequent items in the data based the support you provide to the supp argument. The maxlen defines the maximum number of items in each itemset of frequent items.
frequentItems <- eclat (Groceries, parameter = list(supp = 0.07, maxlen = 15)) # calculates support for frequent items## Eclat
##
## parameter specification:
## tidLists support minlen maxlen target ext
## FALSE 0.07 1 15 frequent itemsets TRUE
##
## algorithmic control:
## sparse sort verbose
## 7 -2 TRUE
##
## Absolute minimum support count: 688
##
## create itemset ...
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
## sorting and recoding items ... [18 item(s)] done [0.00s].
## creating sparse bit matrix ... [18 row(s), 9835 column(s)] done [0.00s].
## writing ... [19 set(s)] done [0.00s].
## Creating S4 object ... done [0.00s].inspect(frequentItems)## items support transIdenticalToItemsets count
## [1] {other vegetables,whole milk} 0.07483477 736 736
## [2] {whole milk} 0.25551601 2513 2513
## [3] {other vegetables} 0.19349263 1903 1903
## [4] {rolls/buns} 0.18393493 1809 1809
## [5] {yogurt} 0.13950178 1372 1372
## [6] {soda} 0.17437722 1715 1715
## [7] {root vegetables} 0.10899847 1072 1072
## [8] {tropical fruit} 0.10493137 1032 1032
## [9] {bottled water} 0.11052364 1087 1087
## [10] {sausage} 0.09395018 924 924
## [11] {shopping bags} 0.09852567 969 969
## [12] {citrus fruit} 0.08276563 814 814
## [13] {pastry} 0.08896797 875 875
## [14] {pip fruit} 0.07564820 744 744
## [15] {whipped/sour cream} 0.07168277 705 705
## [16] {fruit/vegetable juice} 0.07229283 711 711
## [17] {newspapers} 0.07981698 785 785
## [18] {bottled beer} 0.08052872 792 792
## [19] {canned beer} 0.07768175 764 764itemFrequencyPlot(Groceries, topN=10, type="absolute", main="Item Frequency") # plot frequent items
###How to get the product recommendation rules?
rules <- apriori (Groceries, parameter = list(supp = 0.001, conf = 0.5)) # Min Support as 0.001, confidence as 0.8.## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.5 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 10 rules TRUE
##
## 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 ... [5668 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules_conf <- sort (rules, by="confidence", decreasing=TRUE) # 'high-confidence' rules.inspect(head(rules_conf)) # show the support, lift and confidence for all rules## lhs rhs support confidence coverage lift count
## [1] {rice,
## sugar} => {whole milk} 0.001220132 1 0.001220132 3.913649 12
## [2] {canned fish,
## hygiene articles} => {whole milk} 0.001118454 1 0.001118454 3.913649 11
## [3] {root vegetables,
## butter,
## rice} => {whole milk} 0.001016777 1 0.001016777 3.913649 10
## [4] {root vegetables,
## whipped/sour cream,
## flour} => {whole milk} 0.001728521 1 0.001728521 3.913649 17
## [5] {butter,
## soft cheese,
## domestic eggs} => {whole milk} 0.001016777 1 0.001016777 3.913649 10
## [6] {citrus fruit,
## root vegetables,
## soft cheese} => {other vegetables} 0.001016777 1 0.001016777 5.168156 10rules_lift <- sort (rules, by="lift", decreasing=TRUE) # 'high-lift' rules.
inspect(head(rules_lift)) # show the support, lift and confidence for all rules## lhs rhs support confidence coverage lift count
## [1] {Instant food products,
## soda} => {hamburger meat} 0.001220132 0.6315789 0.001931876 18.99565 12
## [2] {soda,
## popcorn} => {salty snack} 0.001220132 0.6315789 0.001931876 16.69779 12
## [3] {flour,
## baking powder} => {sugar} 0.001016777 0.5555556 0.001830198 16.40807 10
## [4] {ham,
## processed cheese} => {white bread} 0.001931876 0.6333333 0.003050330 15.04549 19
## [5] {whole milk,
## Instant food products} => {hamburger meat} 0.001525165 0.5000000 0.003050330 15.03823 15
## [6] {other vegetables,
## curd,
## yogurt,
## whipped/sour cream} => {cream cheese } 0.001016777 0.5882353 0.001728521 14.83409 10The rules with confidence of 1 (see rules_conf above) imply that, whenever the LHS (Itemsets in Antecedent) item was purchased, the RHS (itemsets in consequence)item was also purchased 100% of the time.
A rule with a lift of 18 (see rules_lift above) imply that, the items in LHS and RHS are 18 times more likely to be purchased together compared to the purchases when they are assumed to be unrelated.
###How To Control The Number Of Rules in Output ? Adjust the maxlen, supp and conf arguments in the apriori function to control the number of rules generated. You will have to adjust this based on the sparesness of you data.
rules <- apriori(Groceries, parameter = list (supp = 0.001, conf = 0.2, maxlen=3)) # maxlen = 3 limits the elements in a rule to 3## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.2 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 3 rules TRUE
##
## 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## Warning in apriori(Groceries, parameter = list(supp = 0.001, conf = 0.2, :
## Mining stopped (maxlen reached). Only patterns up to a length of 3 returned!## done [0.00s].
## writing ... [9958 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].#summary of rules
summary(rules)## set of 9958 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3
## 1 620 9337
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 3.000 2.938 3.000 3.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.001017 Min. :0.2000 Min. :0.001118 Min. : 0.8028
## 1st Qu.:0.001220 1st Qu.:0.2439 1st Qu.:0.003762 1st Qu.: 1.8658
## Median :0.001627 Median :0.3077 Median :0.005287 Median : 2.3603
## Mean :0.002554 Mean :0.3452 Mean :0.008272 Mean : 2.6338
## 3rd Qu.:0.002542 3rd Qu.:0.4194 3rd Qu.:0.008236 3rd Qu.: 3.0742
## Max. :0.255516 Max. :1.0000 Max. :1.000000 Max. :35.7158
## count
## Min. : 10.00
## 1st Qu.: 12.00
## Median : 16.00
## Mean : 25.12
## 3rd Qu.: 25.00
## Max. :2513.00
##
## mining info:
## data ntransactions support confidence
## Groceries 9835 0.001 0.2- To get ‘strong‘ rules, increase the value of ‘conf’ parameter.
- To get ’longer‘ rules, increase ’maxlen.
# Inspect rules
#inspect(rules)
#inspect top 5 rules by highest lift
inspect(head(sort(rules, by ="lift"),5))## lhs rhs support
## [1] {bottled beer,red/blush wine} => {liquor} 0.001931876
## [2] {hamburger meat,soda} => {Instant food products} 0.001220132
## [3] {ham,white bread} => {processed cheese} 0.001931876
## [4] {bottled beer,liquor} => {red/blush wine} 0.001931876
## [5] {Instant food products,soda} => {hamburger meat} 0.001220132
## confidence coverage lift count
## [1] 0.3958333 0.004880529 35.71579 19
## [2] 0.2105263 0.005795628 26.20919 12
## [3] 0.3800000 0.005083884 22.92822 19
## [4] 0.4130435 0.004677173 21.49356 19
## [5] 0.6315789 0.001931876 18.99565 12# Visualization of rules
#Plotting rules
plot(rules)## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
# Two key plot
plot(rules , shading="order", control=list(main="two-key plot"))## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
1.Purchase pattern related to beverages (Wine , Beer ) Find subset of rules that has Wine on the right hand side
RulesBev1 <- subset(rules, subset = rhs %ain% "soda")
summary(RulesBev1)## set of 699 rules
##
## rule length distribution (lhs + rhs):sizes
## 2 3
## 64 635
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 3.000 3.000 2.908 3.000 3.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.001017 Min. :0.2000 Min. :0.001322 Min. :1.147
## 1st Qu.:0.001220 1st Qu.:0.2390 1st Qu.:0.004067 1st Qu.:1.371
## Median :0.001627 Median :0.2807 Median :0.005592 Median :1.610
## Mean :0.002408 Mean :0.2994 Mean :0.008989 Mean :1.717
## 3rd Qu.:0.002440 3rd Qu.:0.3415 3rd Qu.:0.009049 3rd Qu.:1.958
## Max. :0.038332 Max. :0.7692 Max. :0.183935 Max. :4.411
## count
## Min. : 10.00
## 1st Qu.: 12.00
## Median : 16.00
## Mean : 23.68
## 3rd Qu.: 24.00
## Max. :377.00
##
## mining info:
## data ntransactions support confidence
## Groceries 9835 0.001 0.2inspect(head(sort(RulesBev1, by ="lift"),5))## lhs rhs support confidence coverage
## [1] {coffee,misc. beverages} => {soda} 0.001016777 0.7692308 0.001321810
## [2] {pastry,misc. beverages} => {soda} 0.001220132 0.6315789 0.001931876
## [3] {chicken,waffles} => {soda} 0.001220132 0.5714286 0.002135231
## [4] {tropical fruit,canned beer} => {soda} 0.001728521 0.5666667 0.003050330
## [5] {bottled water,cake bar} => {soda} 0.001016777 0.5555556 0.001830198
## lift count
## [1] 4.411303 10
## [2] 3.621912 12
## [3] 3.276968 12
## [4] 3.249660 17
## [5] 3.185941 10