1. Association Rule Dr. Jieh-Shan George YEH jsyeh@pu.edu.tw

2. ARULES: MINING ASSOCIATION RULES AND FREQUENT ITEMSETS http://cran.r-project.org/web/packages/arules/index.html

3. Association Rule Association rules are rules presenting association or correlation between itemsets. An association rule is in the form of A => B, where A and B are two disjoint itemsets, referred to respectively as the lhs (left-hand side) and rhs (right-hand side) of the rule. The three most widely-used measures for selecting interesting rules are support, confidence and lift.

4. Association Rule support is the percentage of cases in the data that contains both A and B confidence is the percentage of cases containing A that also contain B lift is the ratio of confidence to the percentage of cases containing B.

6. Example: The Titanic Dataset The Titanic dataset in the datasets package is a 4-dimensional table with summarized information on the fate of passengers on the Titanic according to social class, sex, age and survival. titanic.raw.rdata – http://www.rdatamining.com/data/titanic.raw.rda ta?attredirects=0&d=1 http://www.rdatamining.com/data/titanic.raw.rda ta?attredirects=0&d=1

7. load("titanic.raw.rdata") str(Titanic) table [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154... - attr(*, "dimnames")=List of 4..$ Class : chr [1:4] "1st" "2nd" "3rd" "Crew"..$ Sex : chr [1:2] "Male" "Female"..$ Age : chr [1:2] "Child" "Adult"..$ Survived: chr [1:2] "No" "Yes" df <- as.data.frame(Titanic) head(df)

8. Mining Associations with Apriori Description Mine frequent itemsets, association rules or association hyperedges using the Apriori algorithm. The Apriori algorithm employs level-wise search for frequent itemsets. The implementation of Apriori used includes some improvements (e.g., a prefix tree and item sorting). Usage apriori(data, parameter = NULL, appearance = NULL, control = NULL)

9. Mining Associations with Apriori Arguments data – object of class transactions or any data structure which can be coerced into transactions (e.g., a binary matrix or data.frame). parameter – object of class APparameter or named list. The default behavior is to mine rules with support 0.1, confidence 0.8, and maxlen 10. appearance – object of class APappearance or named list. With this argument item appearance can be restricted. By default all items can appear unrestricted. control – object of class APcontrol or named list. Controls the performance of the mining algorithm (item sorting, etc.)

10. library(arules) # find association rules with default settings rules.all <- apriori(titanic.raw) rules.all inspect(rules.all)

11. rhs containing "Survived" only # rules with rhs containing "Survived" only rules <- apriori(titanic.raw, control = list(verbose=F), parameter = list(minlen=2, supp=0.005, conf=0.8), appearance = list(rhs=c("Survived=No", "Survived=Yes"), default="lhs")) quality(rules) <- round(quality(rules), digits=3) rules.sorted <- sort(rules, by="lift") inspect(rules.sorted)

12. find redundant rules # find redundant rules subset.matrix <- is.subset(rules.sorted, rules.sorted) subset.matrix[lower.tri(subset.matrix, diag=T)] <- NA redundant = 1 which(redundant) # remove redundant rules rules.pruned <- rules.sorted[!redundant] inspect(rules.pruned)

13. ###### rules <- apriori(titanic.raw, parameter = list(minlen=3, supp=0.002, conf=0.2), appearance = list(rhs=c("Survived=Yes"), lhs=c("Class=1st", "Class=2nd", "Class=3rd", "Age=Child", "Age=Adult"), default="none"), control = list(verbose=F)) rules.sorted <- sort(rules, by="confidence") inspect(rules.sorted)

14. ARULESVIZ: VISUALIZING ASSOCIATION RULES AND FREQUENT ITEMSETS http://cran.r-project.org/web/packages/arulesViz/index.html

15. ###Visualizing Association Rules library(arulesViz) plot(rules.all) plot(rules.all, method="grouped") plot(rules.all, method="graph") plot(rules.all, method="graph", control=list(type="items")) plot(rules.all, method="paracoord", control=list(reorder=TRUE))

16. plot(rules.all)

17. plot(rules.all, method="grouped")

18. plot(rules.all, method="graph")

19. plot(rules.all, method="graph", control=list(type="items"))

20. plot(rules.all, method="paracoord", control=list(reorder=TRUE))

21. Other Packages arulesSequences: Mining frequent sequences – http://cran.r- project.org/web/packages/arulesSequences/ind ex.html arulesNBMiner: Mining NB-Frequent Itemsets and NB-Precise Rules – http://cran.r- project.org/web/packages/arulesNBMiner/index. html