Also Observe the change in number of rules for different support,confidence values
install.packages("rmarkdown",repos = "http://cran.us.r-project.org")
## Installing package into 'C:/Users/tswaminathan/Documents/R/win-library/3.5'
## (as 'lib' is unspecified)
## package 'rmarkdown' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\tswaminathan\AppData\Local\Temp\Rtmps9fHBf\downloaded_packages
install.packages("arules",repos = "http://cran.us.r-project.org")
## Installing package into 'C:/Users/tswaminathan/Documents/R/win-library/3.5'
## (as 'lib' is unspecified)
## package 'arules' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\tswaminathan\AppData\Local\Temp\Rtmps9fHBf\downloaded_packages
install.packages("arulesViz",repos = "http://cran.us.r-project.org")
## Installing package into 'C:/Users/tswaminathan/Documents/R/win-library/3.5'
## (as 'lib' is unspecified)
## package 'arulesViz' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\tswaminathan\AppData\Local\Temp\Rtmps9fHBf\downloaded_packages
library(arules)
## Loading required package: Matrix
##
## Attaching package: 'arules'
## The following objects are masked from 'package:base':
##
## abbreviate, write
library(arulesViz)
## Loading required package: grid
mymovies <- read.csv(file.choose())
View(mymovies)
rules <- apriori(as.matrix(mymovies[,6:15],parameter=list(support=0.2, confidence = 0.5,minlen=5)))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.1 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: 1
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[10 item(s), 10 transaction(s)] done [0.00s].
## sorting and recoding items ... [10 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [77 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
# Provided the rules with 2% Support, 50 % Confidence and watched a minimum of 2 movies
rules
## set of 77 rules
inspect(head(sort(rules, by = "lift")))
## lhs rhs support
## [1] {Gladiator,Green.Mile} => {LOTR} 0.1
## [2] {Sixth.Sense,Gladiator,Green.Mile} => {LOTR} 0.1
## [3] {Harry.Potter2} => {Harry.Potter1} 0.1
## [4] {LOTR} => {Green.Mile} 0.1
## [5] {LOTR1} => {LOTR2} 0.2
## [6] {LOTR2} => {LOTR1} 0.2
## confidence lift count
## [1] 1 10 1
## [2] 1 10 1
## [3] 1 5 1
## [4] 1 5 1
## [5] 1 5 2
## [6] 1 5 2
head(quality(rules))
## support confidence lift count
## 1 0.1 1 5.000000 1
## 2 0.1 1 1.666667 1
## 3 0.1 1 1.428571 1
## 4 0.1 1 5.000000 1
## 5 0.1 1 1.428571 1
## 6 0.1 1 1.666667 1
plot(rules,method = "scatterplot")
## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(rules, method = "grouped")
# It looks ike most of them has wateched Lord of the rings movies along with Gladiator and Greenville
# Also most of them has watched Gladiator, Sixth sense along with Patrioit
# Patriot ,Braveheart and other three items along with Gladiator.
plot(rules,method = "graph")
