R語言實戰k-means聚類和關聯規則演算法
阿新 • • 發佈：2019-01-02
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
程式碼：
2、R語言關聯規則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
每列代表一個屬性，表示出現這個屬性，每行代表記錄數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
程式碼如下：
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]）
</pre><pre code_snippet_id="1620120" snippet_file_name="blog_20160322_8_2841846" name="code" class="html">/*Eclat演算法*/<p>rules=apriori(Groceries,parameter=list(support=0.01,confidence=0.01)) #求關聯規則</p><p>summary(rules) #察看求得的關聯規則之摘要</p><p>x=subset(rules,subset=rhs%in%”whole milk”&lift>=1.2) #求所需要的關聯規則子集</p><p>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</p><div>
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