Market Basket Dataset
library(arules)
## Loading required package: Matrix
##
## Attaching package: 'arules'
## The following objects are masked from 'package:base':
##
## abbreviate, write
library(arulesViz)
## Warning: package 'arulesViz' was built under R version 3.5.1
## Loading required package: grid
mydata <- read.csv("C:\\Users\\RISHI RAHUL\\Desktop\\DS\\8 Association Rules\\Assignment\\Market_Basket.csv", header = FALSE)
mydata <- read.transactions('C:\\Users\\RISHI RAHUL\\Desktop\\DS\\8 Association Rules\\Assignment\\Market_Basket.csv', sep = ',', rm.duplicates = TRUE)
## distribution of transactions with duplicates:
## 1
## 5
itemFrequencyPlot(mydata,topN=20)
rules <- apriori(mydata, parameter = list(supp = 0.005, confidence = 0.50, minlen = 2, maxlen = 3))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.5 0.1 1 none FALSE TRUE 5 0.005 2
## maxlen target ext
## 3 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 37
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].
## sorting and recoding items ... [101 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3
## Warning in apriori(mydata, parameter = list(supp = 0.005, confidence =
## 0.5, : Mining stopped (maxlen reached). Only patterns up to a length of 3
## returned!
## done [0.00s].
## writing ... [20 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
inspect(head(sort(rules), n = 10))
## lhs rhs support
## [1] {ground beef,milk} => {mineral water} 0.011065191
## [2] {eggs,ground beef} => {mineral water} 0.010131982
## [3] {frozen vegetables,ground beef} => {mineral water} 0.009198773
## [4] {frozen vegetables,ground beef} => {spaghetti} 0.008665511
## [5] {milk,soup} => {mineral water} 0.008532196
## [6] {milk,olive oil} => {mineral water} 0.008532196
## [7] {chocolate,olive oil} => {mineral water} 0.008265565
## [8] {chicken,chocolate} => {mineral water} 0.007598987
## [9] {soup,spaghetti} => {mineral water} 0.007465671
## [10] {ground beef,pancakes} => {mineral water} 0.007465671
## confidence lift count
## [1] 0.5030303 2.110308 83
## [2] 0.5066667 2.125563 76
## [3] 0.5433071 2.279277 69
## [4] 0.5118110 2.939582 65
## [5] 0.5614035 2.355194 64
## [6] 0.5000000 2.097595 64
## [7] 0.5040650 2.114649 62
## [8] 0.5181818 2.173871 57
## [9] 0.5233645 2.195614 56
## [10] 0.5137615 2.155327 56
plot(head(sort(rules, by = "lift"), n = 5), method = "graph", control = list(cex = 1.3)) #cex = font size
plot(rules)
plot(head(sort(rules, by = "lift"), n = 10), method = "grouped")
