Data <- read.csv("D:\\DataScience\\Assignments\\AssociationRules\\myphonedata1.csv")
library(car)
## Warning: package 'car' was built under R version 3.5.1
## Loading required package: carData
library(carData)
library(arules)
## Warning: package 'arules' was built under R version 3.5.1
## Loading required package: Matrix
##
## Attaching package: 'arules'
## The following object is masked from 'package:car':
##
## recode
## 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
library(mvinfluence)
## Warning: package 'mvinfluence' was built under R version 3.5.1
## Loading required package: heplots
## Warning: package 'heplots' was built under R version 3.5.1
Data1 <- as(Data,"transactions")
# Item Frequency plot
itemFrequencyPlot(Data1,topN=25)
Phone_apriori <- apriori(Data1, parameter = list(supp=0.005, conf=0.45, minlen=2, maxlen=4))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.45 0.1 1 none FALSE TRUE 5 0.005 2
## maxlen target ext
## 4 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 0
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[11 item(s), 10 transaction(s)] done [0.00s].
## sorting and recoding items ... [11 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [41 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
Phone_apriori
## set of 41 rules
inspect(head(sort(Phone_apriori),n=20))
## lhs rhs support confidence lift
## [1] {white=blue} => {green=} 0.4 1.0000000 1.428571
## [2] {green=} => {white=blue} 0.4 0.5714286 1.428571
## [3] {red=white} => {green=} 0.3 1.0000000 1.428571
## [4] {white=white} => {red=red} 0.3 1.0000000 2.000000
## [5] {red=red} => {white=white} 0.3 0.6000000 2.000000
## [6] {green=blue} => {white=white} 0.2 1.0000000 3.333333
## [7] {white=white} => {green=blue} 0.2 0.6666667 3.333333
## [8] {green=blue} => {red=red} 0.2 1.0000000 2.000000
## [9] {white=} => {green=} 0.2 1.0000000 1.428571
## [10] {red=white} => {white=blue} 0.2 0.6666667 1.666667
## [11] {white=blue} => {red=white} 0.2 0.5000000 1.666667
## [12] {white=blue} => {red=red} 0.2 0.5000000 1.000000
## [13] {white=white,green=blue} => {red=red} 0.2 1.0000000 2.000000
## [14] {red=red,green=blue} => {white=white} 0.2 1.0000000 3.333333
## [15] {red=red,white=white} => {green=blue} 0.2 0.6666667 3.333333
## [16] {red=white,white=blue} => {green=} 0.2 1.0000000 1.428571
## [17] {red=white,green=} => {white=blue} 0.2 0.6666667 1.666667
## [18] {white=blue,green=} => {red=white} 0.2 0.5000000 1.666667
## [19] {red=red,white=blue} => {green=} 0.2 1.0000000 1.428571
## [20] {white=blue,green=} => {red=red} 0.2 0.5000000 1.000000
## count
## [1] 4
## [2] 4
## [3] 3
## [4] 3
## [5] 3
## [6] 2
## [7] 2
## [8] 2
## [9] 2
## [10] 2
## [11] 2
## [12] 2
## [13] 2
## [14] 2
## [15] 2
## [16] 2
## [17] 2
## [18] 2
## [19] 2
## [20] 2
inspect(tail(sort(Phone_apriori),n=20))
## lhs rhs support confidence
## [1] {white=orange} => {red=white} 0.1 1.0
## [2] {white=orange} => {green=} 0.1 1.0
## [3] {green=orange} => {white=white} 0.1 1.0
## [4] {green=orange} => {red=red} 0.1 1.0
## [5] {red=green} => {white=} 0.1 1.0
## [6] {white=} => {red=green} 0.1 0.5
## [7] {red=green} => {green=} 0.1 1.0
## [8] {red=yellow} => {white=} 0.1 1.0
## [9] {white=} => {red=yellow} 0.1 0.5
## [10] {red=yellow} => {green=} 0.1 1.0
## [11] {red=white,white=orange} => {green=} 0.1 1.0
## [12] {white=orange,green=} => {red=white} 0.1 1.0
## [13] {white=white,green=orange} => {red=red} 0.1 1.0
## [14] {red=red,green=orange} => {white=white} 0.1 1.0
## [15] {red=green,white=} => {green=} 0.1 1.0
## [16] {red=green,green=} => {white=} 0.1 1.0
## [17] {white=,green=} => {red=green} 0.1 0.5
## [18] {red=yellow,white=} => {green=} 0.1 1.0
## [19] {red=yellow,green=} => {white=} 0.1 1.0
## [20] {white=,green=} => {red=yellow} 0.1 0.5
## lift count
## [1] 3.333333 1
## [2] 1.428571 1
## [3] 3.333333 1
## [4] 2.000000 1
## [5] 5.000000 1
## [6] 5.000000 1
## [7] 1.428571 1
## [8] 5.000000 1
## [9] 5.000000 1
## [10] 1.428571 1
## [11] 1.428571 1
## [12] 3.333333 1
## [13] 2.000000 1
## [14] 3.333333 1
## [15] 1.428571 1
## [16] 5.000000 1
## [17] 5.000000 1
## [18] 1.428571 1
## [19] 5.000000 1
## [20] 5.000000 1
plot(head(sort(Phone_apriori),n=20), method="graph", control=list(cex=0.70))
plot(Phone_apriori)
## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(head(sort(Phone_apriori),n=10), method="grouped", control=list(cex=0.2))
## Warning: Unknown control parameters: cex
## Available control parameters (with default values):
## main = Grouped Matrix for 10 Rules
## k = 20
## rhs_max = 10
## lhs_items = 2
## aggr.fun = function (x, ...) UseMethod("mean")
## col = 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")
## reverse = TRUE
## xlab = NULL
## ylab = NULL
## legend = Size: support Color: lift
## spacing = -1
## panel.function = function (row, size, shading, spacing) { size[size == 0] <- NA shading[is.na(shading)] <- 1 grid.circle(x = c(1:length(size)), y = row, r = size/2 * (1 - spacing), default.units = "native", gp = gpar(fill = shading, col = shading, alpha = 0.9)) }
## gp_main = list(cex = 1.2, fontface = "bold", font = c(bold = 2))
## gp_labels = list(cex = 0.8)
## gp_labs = list(cex = 1.2, fontface = "bold", font = c(bold = 2))
## gp_lines = list(col = "gray", lty = 3)
## newpage = TRUE
## max.shading = NA
## engine = default
## verbose = FALSE
