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Market Basket Analysis in R

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This document provides information about association rule mining on market basket analysis data. It discusses connecting with the company on various platforms, accessing resources like slides and code, and the key concepts of association rule mining including what it is, why it is used, how it works, and example use cases. It then demonstrates the process of generating, inspecting, and filtering rules from transaction data to understand common purchases and influence of products. Top rules are examined by support, confidence, and lift. Association rule mining can uncover frequently bought item sets and has applications in retail and other industries.

Data & Analytics
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Data Science Insights
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Connect With Us Website ( ) Free Online R Courses ( ) R Packages ( ) Shiny Apps ( ) Blog ( ) GitHub ( ) YouTube ( ) Twitter ( ) Facebook ( ) Linkedin ( ) • https://www.rsquaredacademy.com/ • https://rsquared-academy.thinkific.com/ • https://pkgs.rsquaredacademy.com • https://apps.rsquaredacademy.com • https://blog.rsquaredacademy.com • https://github.com/rsquaredacademy • https://www.youtube.com/user/rsquaredin/ • https://twitter.com/rsquaredacademy • https://www.facebook.com/rsquaredacademy/ • https://in.linkedin.com/company/rsquared-academy 2
Resources • Slides • Code & Data • RStudio Cloud 3
What? Why? How? Use Cases Demo • • • • • 4
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Data • UCI • data.world 19
Data Dictionary invoice number stock code description quantity invoice date unit price customer id country • • • • • • • • 20
Libraries library(readxl) library(readr) library(mbar) library(arules) library(arulesViz) library(magrittr) library(dplyr) library(lubridate) library(forcats) library(ggplot2) 21
Read Data basket_data <- read.transactions("transaction_data.csv", format = "baske sep = ",") basket_data ## transactions in sparse format with ## 25901 transactions (rows) and ## 10085 items (columns) 22
Data Summary summary(basket_data) ## transactions as itemMatrix in sparse format with ## 25901 rows (elements/itemsets/transactions) and ## 10085 columns (items) and a density of 0.001660018 ## ## most frequent items: ## WHITE HANGING HEART T-LIGHT HOLDER REGENCY CAKESTAND 3 TIER ## 1999 1914 ## JUMBO BAG RED RETROSPOT PARTY BUNTING ## 1806 1488 ## LUNCH BAG RED RETROSPOT (Other) ## 1404 425005 ## ## element (itemset/transaction) length distribution: ## sizes ## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 ## 1454 4578 1727 1208 942 891 781 715 696 683 612 642 547 530 ## 15 16 17 18 19 20 21 22 23 24 25 26 27 28 ## 555 537 479 459 491 428 405 328 311 280 248 261 235 221 23
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Generate Rules rules <- apriori(basket_data, parameter = list(supp=0.009, conf=0.8, target = "rules", maxlen = 4)) ## Apriori ## ## Parameter specification: ## confidence minval smax arem aval originalSupport maxtime support mi ## 0.8 0.1 1 none FALSE TRUE 5 0.009 ## 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: 233 ## ## set item appearances ...[0 item(s)] done [0.00s]. ## set transactions ...[10085 item(s), 25901 transaction(s)] done [1.41s ## sorting and recoding items [508 item(s)] done [0 03s] ## Warning in apriori(basket_data, parameter = list(supp = 0.009, conf = ## 0.8, : Mining stopped (maxlen reached). Only patterns up to a length ## returned! 25
Rules Summary summary(rules) ## set of 22 rules ## ## rule length distribution (lhs + rhs):sizes ## 2 3 4 ## 11 9 2 ## ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 2.000 2.000 2.500 2.591 3.000 4.000 ## ## summary of quality measures: ## support confidence lift count ## Min. :0.009034 Min. :0.8035 Min. :22.59 Min. :234.0 ## 1st Qu.:0.010453 1st Qu.:0.8530 1st Qu.:25.02 1st Qu.:270.8 ## Median :0.013223 Median :0.8868 Median :55.94 Median :342.5 ## Mean :0.012760 Mean :0.9120 Mean :48.55 Mean :330.5 ## 3rd Qu.:0.014362 3rd Qu.:1.0000 3rd Qu.:61.23 3rd Qu.:372.0 ## Max. :0.018339 Max. :1.0000 Max. :71.30 Max. :475.0 ## 26
Inspect Rules basket_rules <- sort(rules, by = 'confidence', decreasing = TRUE) inspect(basket_rules[1:10]) ## lhs rhs ## [1] {BACK DOOR} => {KEY FOB} ## [2] {SET 3 RETROSPOT TEA} => {SUGAR} ## [3] {SUGAR} => {SET 3 RETROSPOT TEA} ## [4] {SET 3 RETROSPOT TEA} => {COFFEE} ## [5] {SUGAR} => {COFFEE} ## [6] {SHED} => {KEY FOB} ## [7] {SET 3 RETROSPOT TEA, ## SUGAR} => {COFFEE} ## [8] {COFFEE, ## SET 3 RETROSPOT TEA} => {SUGAR} ## [9] {COFFEE, ## SUGAR} => {SET 3 RETROSPOT TEA} ## [10] {PINK REGENCY TEACUP AND SAUCER, ## REGENCY CAKESTAND 3 TIER, ## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND S 27
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Redundant Rules inspect(rules[is.redundant(rules)]) ## lhs rhs support ## [1] {SET 3 RETROSPOT TEA,SUGAR} => {COFFEE} 0.01436238 ## [2] {COFFEE,SET 3 RETROSPOT TEA} => {SUGAR} 0.01436238 ## [3] {COFFEE,SUGAR} => {SET 3 RETROSPOT TEA} 0.01436238 ## confidence lift count ## [1] 1 55.94168 372 ## [2] 1 69.62634 372 ## [3] 1 69.62634 372 31
Non Redundant Rules inspect(rules[!is.redundant(rules)]) ## lhs rhs ## [1] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE GREEN ## [2] {BACK DOOR} => {KEY FOB} ## [3] {SET 3 RETROSPOT TEA} => {SUGAR} ## [4] {SUGAR} => {SET 3 RETROSPOT TEA} ## [5] {SET 3 RETROSPOT TEA} => {COFFEE} ## [6] {COFFEE} => {SET 3 RETROSPOT TEA} ## [7] {SUGAR} => {COFFEE} ## [8] {COFFEE} => {SUGAR} ## [9] {REGENCY TEA PLATE GREEN} => {REGENCY TEA PLATE ROSES ## [10] {SHED} => {KEY FOB} ## [11] {SET/6 RED SPOTTY PAPER CUPS} => {SET/6 RED SPOTTY PAPER ## [12] {SET/20 RED RETROSPOT PAPER NAPKINS, ## SET/6 RED SPOTTY PAPER CUPS} => {SET/6 RED SPOTTY PAPER ## [13] {PINK REGENCY TEACUP AND SAUCER, ## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AN ## [14] {GREEN REGENCY TEACUP AND SAUCER, ## PINK REGENCY TEACUP AND SAUCER} => {ROSES REGENCY TEACUP AN 32
What influenced purchase of product X? sugar_rules <- apriori(basket_data, parameter = list(supp = 0.009, conf appearance = list(default = "lhs", rhs = "SUGAR")) ## Apriori ## ## Parameter specification: ## confidence minval smax arem aval originalSupport maxtime support mi ## 0.8 0.1 1 none FALSE TRUE 5 0.009 ## 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: 233 ## ## set item appearances ...[1 item(s)] done [0.00s]. ## set transactions ...[10085 item(s), 25901 transaction(s)] done [1.32s ## sorting and recoding items [508 item(s)] done [0 03s] 33
What influenced purchase of product X? rules_sugar <- sort(sugar_rules, by = "confidence", decreasing = TRUE) inspect(rules_sugar) ## lhs rhs support confidence lif ## [1] {SET 3 RETROSPOT TEA} => {SUGAR} 0.01436238 1.0000000 69. ## [2] {COFFEE,SET 3 RETROSPOT TEA} => {SUGAR} 0.01436238 1.0000000 69. ## [3] {COFFEE} => {SUGAR} 0.01436238 0.8034557 55. ## count ## [1] 372 ## [2] 372 ## [3] 372 34
What purchases did product X influence? sugar_rules <- apriori(basket_data, parameter = list(supp = 0.009, conf appearance = list(default = "rhs", lhs = "SUGAR")) ## Apriori ## ## Parameter specification: ## confidence minval smax arem aval originalSupport maxtime support mi ## 0.8 0.1 1 none FALSE TRUE 5 0.009 ## 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: 233 ## ## set item appearances ...[1 item(s)] done [0.00s]. ## set transactions ...[10085 item(s), 25901 transaction(s)] done [1.35s ## sorting and recoding items [508 item(s)] done [0 03s] 35
What purchases did product X influence? rules_sugar <- sort(sugar_rules, by = "confidence", decreasing = TRUE) inspect(rules_sugar) ## lhs rhs support confidence lift c ## [1] {SUGAR} => {SET 3 RETROSPOT TEA} 0.01436238 1 69.62634 3 ## [2] {SUGAR} => {COFFEE} 0.01436238 1 55.94168 3 36
Top Rules by Support supp_rules <- sort(rules, by = 'support', decreasing = TRUE) top_rules <- supp_rules[1:10] inspect(top_rules) ## lhs rhs ## [1] {PINK REGENCY TEACUP AND SAUCER, ## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND S ## [2] {GREEN REGENCY TEACUP AND SAUCER, ## PINK REGENCY TEACUP AND SAUCER} => {ROSES REGENCY TEACUP AND S ## [3] {SET 3 RETROSPOT TEA} => {SUGAR} ## [4] {SUGAR} => {SET 3 RETROSPOT TEA} ## [5] {SET 3 RETROSPOT TEA} => {COFFEE} ## [6] {COFFEE} => {SET 3 RETROSPOT TEA} ## [7] {SUGAR} => {COFFEE} ## [8] {COFFEE} => {SUGAR} ## [9] {SET 3 RETROSPOT TEA, ## SUGAR} => {COFFEE} ## [10] {COFFEE, ## SET 3 RETROSPOT TEA} => {SUGAR} 37
Top Rules by Confidence conf_rules <- sort(rules, by = 'confidence', decreasing = TRUE) top_rules <- conf_rules[1:10] inspect(top_rules) ## lhs rhs ## [1] {BACK DOOR} => {KEY FOB} ## [2] {SET 3 RETROSPOT TEA} => {SUGAR} ## [3] {SUGAR} => {SET 3 RETROSPOT TEA} ## [4] {SET 3 RETROSPOT TEA} => {COFFEE} ## [5] {SUGAR} => {COFFEE} ## [6] {SHED} => {KEY FOB} ## [7] {SET 3 RETROSPOT TEA, ## SUGAR} => {COFFEE} ## [8] {COFFEE, ## SET 3 RETROSPOT TEA} => {SUGAR} ## [9] {COFFEE, ## SUGAR} => {SET 3 RETROSPOT TEA} ## [10] {PINK REGENCY TEACUP AND SAUCER, ## REGENCY CAKESTAND 3 TIER, ## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND S 38
Top Rules by Lift lift_rules <- sort(rules, by = 'lift', decreasing = TRUE) top_rules <- lift_rules[1:10] inspect(top_rules) ## lhs rhs suppo ## [1] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE GREEN} 0.0090344 ## [2] {SET 3 RETROSPOT TEA} => {SUGAR} 0.0143623 ## [3] {SUGAR} => {SET 3 RETROSPOT TEA} 0.0143623 ## [4] {COFFEE, ## SET 3 RETROSPOT TEA} => {SUGAR} 0.0143623 ## [5] {COFFEE, ## SUGAR} => {SET 3 RETROSPOT TEA} 0.0143623 ## [6] {BACK DOOR} => {KEY FOB} 0.0096135 ## [7] {SHED} => {KEY FOB} 0.0112736 ## [8] {REGENCY TEA PLATE GREEN} => {REGENCY TEA PLATE ROSES} 0.0103470 ## [9] {SET 3 RETROSPOT TEA} => {COFFEE} 0.0143623 ## [10] {COFFEE} => {SET 3 RETROSPOT TEA} 0.0143623 39
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Summary unsupervised data mining technique uncovers products frequently bought together creates if-then scenario rules cost-effective, insightful and actionable association rule mining has applications in several industries directionality of rule is lost while using lift confidence as a measure can be misleading • • • • • • • 46
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Market Basket Analysis in R

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  • 20. Data Dictionary invoice number stock code description quantity invoice date unit price customer id country • • • • • • • • 20
  • 22. Read Data basket_data <- read.transactions("transaction_data.csv", format = "baske sep = ",") basket_data ## transactions in sparse format with ## 25901 transactions (rows) and ## 10085 items (columns) 22
  • 23. Data Summary summary(basket_data) ## transactions as itemMatrix in sparse format with ## 25901 rows (elements/itemsets/transactions) and ## 10085 columns (items) and a density of 0.001660018 ## ## most frequent items: ## WHITE HANGING HEART T-LIGHT HOLDER REGENCY CAKESTAND 3 TIER ## 1999 1914 ## JUMBO BAG RED RETROSPOT PARTY BUNTING ## 1806 1488 ## LUNCH BAG RED RETROSPOT (Other) ## 1404 425005 ## ## element (itemset/transaction) length distribution: ## sizes ## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 ## 1454 4578 1727 1208 942 891 781 715 696 683 612 642 547 530 ## 15 16 17 18 19 20 21 22 23 24 25 26 27 28 ## 555 537 479 459 491 428 405 328 311 280 248 261 235 221 23
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  • 25. Generate Rules rules <- apriori(basket_data, parameter = list(supp=0.009, conf=0.8, target = "rules", maxlen = 4)) ## Apriori ## ## Parameter specification: ## confidence minval smax arem aval originalSupport maxtime support mi ## 0.8 0.1 1 none FALSE TRUE 5 0.009 ## 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: 233 ## ## set item appearances ...[0 item(s)] done [0.00s]. ## set transactions ...[10085 item(s), 25901 transaction(s)] done [1.41s ## sorting and recoding items [508 item(s)] done [0 03s] ## Warning in apriori(basket_data, parameter = list(supp = 0.009, conf = ## 0.8, : Mining stopped (maxlen reached). Only patterns up to a length ## returned! 25
  • 26. Rules Summary summary(rules) ## set of 22 rules ## ## rule length distribution (lhs + rhs):sizes ## 2 3 4 ## 11 9 2 ## ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 2.000 2.000 2.500 2.591 3.000 4.000 ## ## summary of quality measures: ## support confidence lift count ## Min. :0.009034 Min. :0.8035 Min. :22.59 Min. :234.0 ## 1st Qu.:0.010453 1st Qu.:0.8530 1st Qu.:25.02 1st Qu.:270.8 ## Median :0.013223 Median :0.8868 Median :55.94 Median :342.5 ## Mean :0.012760 Mean :0.9120 Mean :48.55 Mean :330.5 ## 3rd Qu.:0.014362 3rd Qu.:1.0000 3rd Qu.:61.23 3rd Qu.:372.0 ## Max. :0.018339 Max. :1.0000 Max. :71.30 Max. :475.0 ## 26
  • 27. Inspect Rules basket_rules <- sort(rules, by = 'confidence', decreasing = TRUE) inspect(basket_rules[1:10]) ## lhs rhs ## [1] {BACK DOOR} => {KEY FOB} ## [2] {SET 3 RETROSPOT TEA} => {SUGAR} ## [3] {SUGAR} => {SET 3 RETROSPOT TEA} ## [4] {SET 3 RETROSPOT TEA} => {COFFEE} ## [5] {SUGAR} => {COFFEE} ## [6] {SHED} => {KEY FOB} ## [7] {SET 3 RETROSPOT TEA, ## SUGAR} => {COFFEE} ## [8] {COFFEE, ## SET 3 RETROSPOT TEA} => {SUGAR} ## [9] {COFFEE, ## SUGAR} => {SET 3 RETROSPOT TEA} ## [10] {PINK REGENCY TEACUP AND SAUCER, ## REGENCY CAKESTAND 3 TIER, ## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND S 27
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  • 31. Redundant Rules inspect(rules[is.redundant(rules)]) ## lhs rhs support ## [1] {SET 3 RETROSPOT TEA,SUGAR} => {COFFEE} 0.01436238 ## [2] {COFFEE,SET 3 RETROSPOT TEA} => {SUGAR} 0.01436238 ## [3] {COFFEE,SUGAR} => {SET 3 RETROSPOT TEA} 0.01436238 ## confidence lift count ## [1] 1 55.94168 372 ## [2] 1 69.62634 372 ## [3] 1 69.62634 372 31
  • 32. Non Redundant Rules inspect(rules[!is.redundant(rules)]) ## lhs rhs ## [1] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE GREEN ## [2] {BACK DOOR} => {KEY FOB} ## [3] {SET 3 RETROSPOT TEA} => {SUGAR} ## [4] {SUGAR} => {SET 3 RETROSPOT TEA} ## [5] {SET 3 RETROSPOT TEA} => {COFFEE} ## [6] {COFFEE} => {SET 3 RETROSPOT TEA} ## [7] {SUGAR} => {COFFEE} ## [8] {COFFEE} => {SUGAR} ## [9] {REGENCY TEA PLATE GREEN} => {REGENCY TEA PLATE ROSES ## [10] {SHED} => {KEY FOB} ## [11] {SET/6 RED SPOTTY PAPER CUPS} => {SET/6 RED SPOTTY PAPER ## [12] {SET/20 RED RETROSPOT PAPER NAPKINS, ## SET/6 RED SPOTTY PAPER CUPS} => {SET/6 RED SPOTTY PAPER ## [13] {PINK REGENCY TEACUP AND SAUCER, ## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AN ## [14] {GREEN REGENCY TEACUP AND SAUCER, ## PINK REGENCY TEACUP AND SAUCER} => {ROSES REGENCY TEACUP AN 32
  • 33. What influenced purchase of product X? sugar_rules <- apriori(basket_data, parameter = list(supp = 0.009, conf appearance = list(default = "lhs", rhs = "SUGAR")) ## Apriori ## ## Parameter specification: ## confidence minval smax arem aval originalSupport maxtime support mi ## 0.8 0.1 1 none FALSE TRUE 5 0.009 ## 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: 233 ## ## set item appearances ...[1 item(s)] done [0.00s]. ## set transactions ...[10085 item(s), 25901 transaction(s)] done [1.32s ## sorting and recoding items [508 item(s)] done [0 03s] 33
  • 34. What influenced purchase of product X? rules_sugar <- sort(sugar_rules, by = "confidence", decreasing = TRUE) inspect(rules_sugar) ## lhs rhs support confidence lif ## [1] {SET 3 RETROSPOT TEA} => {SUGAR} 0.01436238 1.0000000 69. ## [2] {COFFEE,SET 3 RETROSPOT TEA} => {SUGAR} 0.01436238 1.0000000 69. ## [3] {COFFEE} => {SUGAR} 0.01436238 0.8034557 55. ## count ## [1] 372 ## [2] 372 ## [3] 372 34
  • 35. What purchases did product X influence? sugar_rules <- apriori(basket_data, parameter = list(supp = 0.009, conf appearance = list(default = "rhs", lhs = "SUGAR")) ## Apriori ## ## Parameter specification: ## confidence minval smax arem aval originalSupport maxtime support mi ## 0.8 0.1 1 none FALSE TRUE 5 0.009 ## 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: 233 ## ## set item appearances ...[1 item(s)] done [0.00s]. ## set transactions ...[10085 item(s), 25901 transaction(s)] done [1.35s ## sorting and recoding items [508 item(s)] done [0 03s] 35
  • 36. What purchases did product X influence? rules_sugar <- sort(sugar_rules, by = "confidence", decreasing = TRUE) inspect(rules_sugar) ## lhs rhs support confidence lift c ## [1] {SUGAR} => {SET 3 RETROSPOT TEA} 0.01436238 1 69.62634 3 ## [2] {SUGAR} => {COFFEE} 0.01436238 1 55.94168 3 36
  • 37. Top Rules by Support supp_rules <- sort(rules, by = 'support', decreasing = TRUE) top_rules <- supp_rules[1:10] inspect(top_rules) ## lhs rhs ## [1] {PINK REGENCY TEACUP AND SAUCER, ## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND S ## [2] {GREEN REGENCY TEACUP AND SAUCER, ## PINK REGENCY TEACUP AND SAUCER} => {ROSES REGENCY TEACUP AND S ## [3] {SET 3 RETROSPOT TEA} => {SUGAR} ## [4] {SUGAR} => {SET 3 RETROSPOT TEA} ## [5] {SET 3 RETROSPOT TEA} => {COFFEE} ## [6] {COFFEE} => {SET 3 RETROSPOT TEA} ## [7] {SUGAR} => {COFFEE} ## [8] {COFFEE} => {SUGAR} ## [9] {SET 3 RETROSPOT TEA, ## SUGAR} => {COFFEE} ## [10] {COFFEE, ## SET 3 RETROSPOT TEA} => {SUGAR} 37
  • 38. Top Rules by Confidence conf_rules <- sort(rules, by = 'confidence', decreasing = TRUE) top_rules <- conf_rules[1:10] inspect(top_rules) ## lhs rhs ## [1] {BACK DOOR} => {KEY FOB} ## [2] {SET 3 RETROSPOT TEA} => {SUGAR} ## [3] {SUGAR} => {SET 3 RETROSPOT TEA} ## [4] {SET 3 RETROSPOT TEA} => {COFFEE} ## [5] {SUGAR} => {COFFEE} ## [6] {SHED} => {KEY FOB} ## [7] {SET 3 RETROSPOT TEA, ## SUGAR} => {COFFEE} ## [8] {COFFEE, ## SET 3 RETROSPOT TEA} => {SUGAR} ## [9] {COFFEE, ## SUGAR} => {SET 3 RETROSPOT TEA} ## [10] {PINK REGENCY TEACUP AND SAUCER, ## REGENCY CAKESTAND 3 TIER, ## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND S 38
  • 39. Top Rules by Lift lift_rules <- sort(rules, by = 'lift', decreasing = TRUE) top_rules <- lift_rules[1:10] inspect(top_rules) ## lhs rhs suppo ## [1] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE GREEN} 0.0090344 ## [2] {SET 3 RETROSPOT TEA} => {SUGAR} 0.0143623 ## [3] {SUGAR} => {SET 3 RETROSPOT TEA} 0.0143623 ## [4] {COFFEE, ## SET 3 RETROSPOT TEA} => {SUGAR} 0.0143623 ## [5] {COFFEE, ## SUGAR} => {SET 3 RETROSPOT TEA} 0.0143623 ## [6] {BACK DOOR} => {KEY FOB} 0.0096135 ## [7] {SHED} => {KEY FOB} 0.0112736 ## [8] {REGENCY TEA PLATE GREEN} => {REGENCY TEA PLATE ROSES} 0.0103470 ## [9] {SET 3 RETROSPOT TEA} => {COFFEE} 0.0143623 ## [10] {COFFEE} => {SET 3 RETROSPOT TEA} 0.0143623 39
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  • 46. Summary unsupervised data mining technique uncovers products frequently bought together creates if-then scenario rules cost-effective, insightful and actionable association rule mining has applications in several industries directionality of rule is lost while using lift confidence as a measure can be misleading • • • • • • • 46
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