R语言实战k-means聚类和关联规则算法

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蓝鲁BP9G39,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
蓝鲁B7M827,1,23,0,1,0,0,2,55,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
蓝鲁BQ3M79,0,11,0,0,0,0,1,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
蓝鲁BU008P,0,4,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
蓝鲁BW6710,14,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0
蓝鲁BS180G,0,1,0,0,0,0,0,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
蓝鲁B3HU73,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

library(fpc)
data<-read.csv('x.csv')
df<-data[2:31]
set.seed(252964)
(kmeans <- kmeans(na.omit(df), 100))
plotcluster(na.omit(df), kmeans$cluster)   #作图
kmeans           #表示查看聚类结果
kmeans$cluster   #表示查看聚类结果
kmeans$center    #表示查看聚类中心
write.csv(kmeans$cluster,'100classes.csv') #将聚类的结果写入到文件中

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0
0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0
0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0

library(arules)
groceries <- read.transactions("groceries.csv")
summary(groceries）

</pre><pre code_snippet_id="1620120" snippet_file_name="blog_20160322_6_7367204" name="code" class="html">/*Apriori算法*/
frequentsets=eclat(Groceries,parameter=list(support=0.05,maxlen=10)) #求频繁项集
inspect(frequentsets[1:10]) #察看求得的频繁项集
inspect(sort(frequentsets,by=”support”)[1:10]) #根据支持度对求得的频繁项集排序并察看（等价于inspect(sort(frequentsets)[1:10]）

</pre><pre code_snippet_id="1620120" snippet_file_name="blog_20160322_8_2841846" name="code" class="html">/*Eclat算法*/

<p>rules=apriori(Groceries,parameter=list(support=0.01,confidence=0.01)) #求关联规则</p><p>summary(rules) #察看求得的关联规则之摘要</p><p>x=subset(rules,subset=rhs%in%”whole milk”&lift>=1.2) #求所需要的关联规则子集</p><p>inspect(sort(x,by=”support”)[1:5]) #根据支持度对求得的关联规则子集排序并察看</p><div>
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