吴喜之R软件教程关联规则分析

1、关联规则分析(association analysis) 1超市例子 例3.1 (Groceries.txt) 这是一个超市购物例子(Hahsler et al., 2006)，数据中有9835笔交易，涉及169种商品。每个交易为一个顾客的购买记录，而每种商品是一个二分变量，比如，购买用1代表，未购买用0代表。通过对数据的初步计算，我们发现在单项计数中，全牛奶(whole milk)的频数最高，为2513(频率接近26%)，而其次为：其它蔬菜(other vegetables)为1903，面包(rolls/buns)为1809，苏打(soda)为1715，酸奶(yogurt)为1372等等。超

2、过5%的顾客购买的商品频率显示在图3.1中。此外，还可以知道分别买不同数量商品的顾客人数，购买1至9种商品的人数展示在下表中：2library(arules)data(Groceries)summary(Groceries)itemFrequencyPlot(Groceries, support = 0.05, s = 0.8) #图3.1超过5%的顾客购买的商品名字和频率 34术语每一个观测称为一个事务或交易(transaction)每一个二分变量称为一个项目或项(item)事务数据集、项目集或项集(itemset)用X表示一个项目或者项目集，用Y表示与X没有交的另一个项目或

3、项目集，那么记号“X=Y”表示X和Y同时出现的一个规则(rule)在X=Y中，称X为前项(也称为条件项或左项，antecedent, left-hand-side or LHS of the rule)，而称Y为后项(也称为结果项或右项，consequent，right-hand-side or RHS of the rule)。5信息 X=Y的支持度(support) X=Y的置信度(confidence) X=Y的提升(lift) 记s(Z)表示事务Z在包含N个事务的整个事务数据集中的频数，用A表示事务包含X的事件，而B表示事务包含Y的事件(X和Y没有交) ，则： 6library(aru

4、les)data(Groceries)summary(Groceries)itemFrequencyPlot(Groceries, support = 0.05, s = 0.8) #图3.1fsets 1.2)inspect(sort(x, by = support)1:5) #第三章表inspect(sort(x, by = confidence)1:5)#第三章表#inspect(sort(x, by = lift)1:5)789x=subset(rules, subset = lhs %in% whole milk &lift 1.2)inspect(sort(x, b

5、y = support)1:5) inspect(sort(x, by = confidence)1:5)#inspect(sort(x, by = lift)1:5)x=subset(rules, subset = lhs %ain% whole milk &lift 1.2)inspect(sort(x, by = support)1:5) inspect(sort(x, by = confidence)1:5)#inspect(sort(x, by = lift)1:5)x=subset(rules, subset = rhs %ain% whole milk &lift 1.2)ins

6、pect(sort(x, by = support)1:5) inspect(sort(x, by = confidence)1:5)#inspect(sort(x, by = lift)1:5)x=subset(rules, subset = lhs %pin% whole milk &lift 1.2)inspect(sort(x, by = support)1:5) inspect(sort(x, by = confidence)1:5)#inspect(sort(x, by = lift)1:5)x=subset(rules, subset = rhs %pin% whole milk

7、 &lift 1.2)inspect(sort(x, by = support)1:5) inspect(sort(x, by = confidence)1:5)#inspect(sort(x, by = lift)1:5)10111213library(arules);w=read.table(f:/xzwu/adbook/shopping.txt,header=TRUE,sep=t);a=w1:10;dim(a)1 786 10 names(a) 1 Ready.made Frozen.foods Alcohol Fresh.Vegetables Milk 6 Bakery.goods F

8、resh.meat Toiletries Snacks Tinned.goods a=as.matrix(a); trans2 - as(a, transactions); summary(trans2)#数据概况14#图示数据itemFrequencyPlot(trans2, support = 0.1, s = 0.8)15fsets 1.2)规则排序：inspect(sort(x, by = confidence)1:3)19连续变量(先变成分类变量)data(AdultUCI)#library(arules)attributes(AdultUCI)$class;attr

9、ibutes(AdultUCI)$names;dim(AdultUCI);AdultUCI1:2, 连续变量处理：删除AdultUCIfnlwgt - NULLAdultUCIeducation-num - NULL分级20连续变量#连续变量处理：#删除AdultUCIfnlwgt - NULLAdultUCIeducation-num - NULL#分级AdultUCIage - ordered(cut(AdultUCIage, c(15,25, 45, 65, 100), labels = c(Young, Middle-aged,Senior, Old)AdultUCIhours-per

10、-week - ordered(cut(AdultUCIhours-per-week, c(0, 25, 40, 60, 168), labels = c(Part-time, Full-time, Over-time, Workaholic)AdultUCIcapital-gain 0), Inf), labels = c(None, Low, High)AdultUCIcapital-loss 0), Inf), labels = c(none, low, high)Adult 1.2)inspect(sort(x, by = confidence)1:5)inspect(sort(x,

11、by = lift)1:5)21summary(Adult)itemFrequencyPlot(Adult, support = 0.5, s = 0.8)22rules = apriori(Adult, parameter = list(support = 0.01,confidence = 0.6)x=subset(rules, subset = rhs %in% income=large &lift 1.2)inspect(sort(x, by = confidence)1:5)inspect(sort(x, by = lift)1:5)23例3.2 (Adult.txt

12、)美国普查局政府网站的数据库的例子。原本有48842个观测及15个变量。这15个变量经过挑选并转换成115个二分变量。 #Adult.txt数据#library(arules)data(Adult)summary(Adult)rules - apriori(Adult, parameter = list(support = 0.01,confidence = 0.6)summary(rules)rulesIncomeSmall 1.2)rulesIncomeLarge 1.2)inspect(sort(rulesIncomeSmall, by = confidence)1:3)inspect(sort(rulesIncomeLarge, by = confidence)1:3)2425Shuttle数据（需要变成二分变量的数据）#shuttle数据#library(MASS);shuttle1:10,summary(shuttle)library(arules)w - as(shuttle, transactions); summary(w)rules