R語言Apriori算法

• 項(xiàng)目要求：
• Project Start
• **規(guī)則生成和可視化**

我們需要安裝arules and arulesViz包。

項(xiàng)目要求：

生成頻繁項(xiàng)目集滿足下面條件：
– The minimum support threshold as 0.02
– The minimum length of the itemsets as 1
– The maximum length of the itemsets as 10

生成的關(guān)聯(lián)規(guī)則滿足下面條件：
– The minimum support threshold as 0.001
– The minimum confidence threshold as 0.6

Project Start

# 下載并加載相關(guān)的包（可能需要梯子） install.packages('arules') install.packages('arulesViz') library('arules') library('arulesViz') # 讀取并展示“Groceries”包 data(Groceries) Groceries summary(Groceries) class(Groceries)

可以看到這個(gè)“Groceries”包一共有9835行和169列。

# 展示前20個(gè)grocery labels Groceries@itemInfo[1:20,]# 顯示第10至20的transactions apply(Groceries@data[,10:20], 2, function(r) paste(Groceries@itemInfo[r,"labels"], collapse=", ") )

接下來是生成頻繁項(xiàng)目集
參數(shù)的設(shè)置按照要求

# frequent 1-itemsets itemsets <- apriori(Groceries, parameter=list(minlen=1, maxlen=1, support=0.02, target="frequent itemsets")) summary(itemsets) inspect(head(sort(itemsets, by = "support"), 10))# frequent 2-itemsets itemsets <- apriori(Groceries, parameter=list(minlen=2, maxlen=2, support=0.02, target="frequent itemsets")) summary(itemsets) inspect(head(sort(itemsets, by ="support"),10))# frequent 3-itemsets itemsets <- apriori(Groceries, parameter=list(minlen=3, maxlen=3, support=0.02, target="frequent itemsets")) inspect(sort(itemsets, by ="support"))# frequent 4-itemsets itemsets <- apriori(Groceries, parameter=list(minlen=4, maxlen=4, support=0.02, target="frequent itemsets")) inspect(sort(itemsets, by ="support"))

生成的頻繁項(xiàng)目集結(jié)果如下：

規(guī)則生成和可視化

rules <- apriori(Groceries, parameter=list(support=0.001,confidence=0.6, target = "rules")) summary(rules) plot(rules) plot(rules@quality)

# 顯示rules與最高lift scores inspect(head(sort(rules, by="lift"), 10))

篩選出置信度大于0.9的rules

confidentRules <- rules[quality(rules)$confidence > 0.9] confidentRulesplot(confidentRules, method="matrix", measure=c("lift", "confidence"), control=list(recorder=TRUE))

一個(gè)127個(gè)，之后polt成為matrix得到

# 選擇lift最高的5項(xiàng)規(guī)則 highLiftRules <- head(sort(rules, by="lift"), 5) plot(highLiftRules, method="graph", control=list(type="items"))

得到圖片如下所示。
從圖中我們可以得到買火腿的基本上都會(huì)購(gòu)買加工芝士，買爆米花或者soda飲料的很大概率會(huì)購(gòu)買咸味小吃。

參考書目

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, EMC Education Services, John Wiley & Sons, 27 Jan. 2015

總結(jié)

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