关联规则demo

library(arules)  # association ruleslibrary(arulesViz)  # data visualization of association ruleslibrary(RColorBrewer)groceries <- read.transactions("groceries.csv", format="basket", sep=",")  summary(groceries) #########进一步查看数据集的信息 class(groceries)  groceriesdim(groceries)  colnames(groceries)[1:5]  rownames(groceries)[1:5]  basketSize<-size(groceries)  summary(basketSize) sum(basketSize) #count of all 1s in the sparse matrix  itemFreq <- itemFrequency(groceries)  sum(itemFreq) #本质上代表"平均一个transaction购买的item个数"itemCount <- (itemFreq/sum(itemFreq))*sum(basketSize)#####itemCount表示每个item出现的次数。Support(X) = Xs / N, N是总的交易数，Xs就是Item X###的count。itemXCount = N * itemXFreq = (ItemXFreq / sum(itemFreq)) * sum(basketSize)  ##summary(itemCount) orderedItem <- sort(itemCount, decreasing = )  orderedItem[1:10]orderedItemFreq <- sort(itemFrequency(groceries), decreasing=)  orderedItemFreq[1:10]  itemFrequencyPlot(groceries, support=0.1)  itemFrequencyPlot(groceries, topN=10, horiz=T)  inspect(groceries[1:5])   image(groceries[1:10])  image(sample(groceries,100)) #############groceryrules <- apriori(groceries, parameter = list(support = 0.006, confidence = 0.25, minlen = 2))  ######inspect查看规则记录inspect(groceryrules[1:10])##########评估ordered_groceryrules <- sort(groceryrules, by="lift")  inspect(ordered_groceryrules[1:5])  ####### 搜索规则yogurtrules <- subset(groceryrules, items %in% c("yogurt"))  inspect(yogurtrules) ###########qualityMeasures<-interestMeasure(groceryrules,c(“coverage”,”fishersExactTest”,”conviction”, “chiSquared”), transactions=groceries)summary(qualityMeasures)########write(groceryrules, file=”groceryrules.csv”, sep=”,”, quote=TRUE, row.names=FALSE)转化data.frame分析：groceryrules_df <- as(groceryrules, “data.frame”)?str(groceryrules_df)########### 可视化plot(groceryrules, method=”scatterplot”,control=list(jitter=2, col = rev(brewer.pal(9, “Greens”))), shading = “lift”)plot(groceryrules, control=list(jitter=2, col = rev(brewer.pal(9, “Greens”))), shading = “lift”,method = ‘grouped’)