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首頁精彩閱讀R語言實戰(zhàn)k-means聚類和關聯(lián)規(guī)則算法
R語言實戰(zhàn)k-means聚類和關聯(lián)規(guī)則算法
2017-05-29
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R語言實戰(zhàn)k-means聚類和關聯(lián)規(guī)則算法

 1、R語言關于k-means聚類
數(shù)據(jù)集格式如下所示：
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    ,河東路與岙東路&河東路與聚賢橋路,河東路與岙東路&新悅路與岙東路,河東路與岙東路&火炬路與聚賢橋路,河東路與岙東路&火炬路與匯智橋路,河東路與岙東路&匯智橋與智力島路,新悅路與岙東路&火炬路與聚賢橋路,新悅路與岙東路&河東路與聚賢橋路,新悅路與岙東路&河東路與岙東路,新悅路與岙東路&匯智橋與智力島路,新悅路與岙東路&火炬路與匯智橋路,河東路與聚賢橋路&新悅路與岙東路,河東路與聚賢橋路&火炬路與聚賢橋路,河東路與聚賢橋路&河東路與岙東路,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&新悅路與岙東路,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&河東路與岙東路,匯智橋與智力島路&新悅路與岙東路,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&河東路與岙東路,匯智橋與智力島路&河東路與聚賢橋路,火炬路與聚賢橋路&新悅路與岙東路,火炬路與聚賢橋路&河東路與岙東路,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  


代碼：

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    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$cluster)   #作圖  
    kmeans           #表示查看聚類結(jié)果  
    kmeans$cluster   #表示查看聚類結(jié)果  
    kmeans$center    #表示查看聚類中心  
    write.csv(kmeans$cluster,'100classes.csv') #將聚類的結(jié)果寫入到文件中  

2、R語言關聯(lián)規(guī)則

數(shù)據(jù)集格式

[plain] view plain copy

    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  

每列代表一個屬性，表示出現(xiàn)這個屬性，每行代表記錄數(shù)

代碼如下：

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

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    </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(jù)支持度對求得的頻繁項集排序并察看（等價于inspect(sort(frequentsets)[1:10]）  

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

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    <p>rules=apriori(Groceries,parameter=list(support=0.01,confidence=0.01)) #求關聯(lián)規(guī)則</p><p>summary(rules) #察看求得的關聯(lián)規(guī)則之摘要</p><p>x=subset(rules,subset=rhs%in%”whole milk”&lift>=1.2) #求所需要的關聯(lián)規(guī)則子集</p><p>inspect(sort(x,by=”support”)[1:5]) #根據(jù)支持度對求得的關聯(lián)規(guī)則子集排序并察看</p><div>  
    </div> 

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