R語言實戰k means聚類和關聯規則演算法

1、r語言關於k-means聚類

資料集格式如下所示：

,河東路與嶴東路&河東路與聚賢橋路,河東路與嶴東路&新悅路與嶴東路,河東路與嶴東路&火炬路與聚賢橋路,河東路與嶴東路&火炬路與匯智橋路,河東路與嶴東路&匯智橋與智力島路,新悅路與嶴東路&火炬路與聚賢橋路,新悅路與嶴東路&河東路與聚賢橋路,新悅路與嶴東路&河東路與嶴東路,新悅路與嶴東路&匯智橋與智力島路,新悅路與嶴東路&火炬路與匯智橋路,河東路與聚賢橋路&新悅路與嶴東路,河東路與聚賢橋路&火炬路與聚賢橋路,河東路與聚賢橋路&河東路與嶴東路,河東路與聚賢橋路&匯智橋與智力島路,河東路與聚賢橋路&火炬路與匯智橋路,火炬路與匯智橋路&新悅路與嶴東路,火炬路與匯智橋路&火炬路與聚賢橋路,火炬路與匯智橋路&匯智橋與智力島路,火炬路與匯智橋路&河東路與聚賢橋路,火炬路與匯智橋路&河東路與嶴東路,匯智橋與智力島路&新悅路與嶴東路,匯智橋與智力島路&火炬路與聚賢橋路,匯智橋與智力島路&火炬路與匯智橋路,匯智橋與智力島路&河東路與嶴東路,匯智橋與智力島路&河東路與聚賢橋路,火炬路與聚賢橋路&新悅路與嶴東路,火炬路與聚賢橋路&河東路與嶴東路,火炬路與聚賢橋路&河東路與聚賢橋路,火炬路與聚賢橋路&匯智橋與智力島路,火炬路與聚賢橋路&火炬路與匯智橋路

藍魯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
藍魯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
藍魯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
藍魯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
藍魯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
藍魯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
藍魯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$cluster) #作圖
kmeans #表示檢視聚類結果
kmeans$cluster #表示檢視聚類結果
kmeans$center #表示檢視聚類中心
write.csv(kmeans$cluster,'100classes.csv') #將聚類的結果寫入到檔案中

資料集格式

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

**如下：

library(arules)

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

/*apriori演算法*/

frequentsets=eclat(groceries,parameter=list(support=0.05,maxlen=10)) #求頻繁項集
inspect(frequentsets[1:10]) #察看求得的頻繁項集
inspect(sort(frequentsets,by=」support」)[1:10]) #根據支援度對求得的頻繁項集排序並察看（等價於inspect(sort(frequentsets)[1:10]）

/*eclat演算法*/

rules=apriori(groceries,parameter=list(support=0.01,confidence=0.01)) #求關聯規則

summary(rules) #察看求得的關聯規則之摘要
x=subset(rules,subset=rhs%in%」whole milk」&lift>=1.2) #求所需要的關聯規則子集
inspect(sort(x,by=」support」)[1:5]) #根據支援度對求得的關聯規則子集排序並察看

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