R language Combat K-means clustering and association rules algorithm

1, R language about K-means clustering

The data set format is as follows:

, Hedong Road and Ao Dong Lu & Hedong Road and Poly-Xian Bridge Road, Hedong Road and AO East Road & New Yue Road and Ao Road, Hedong and Ao Dong Lu & Torch Road and Poly-Xian Bridge Road, Hedong Road and Ao Dong Lu & Torch Road and Hui Zhi Qiao Road, Hedong Road and Ao Road & Hui Zhi Qiao and Intellectual Island Road, Xin Yue Road and Ao Dong Road & Torch Road and the Poly-Xian Bridge Road, New Yue Road and Ao Dong Road & Hedong Lu and Poly Xian Bridge Road, New Yue Road and AO East Road & Hedong Rd and Ao Dong Lu, New Yue Road and AO East Street & Wisdom Bridge and Intellectual Island Road, New Yue Road and AO East Road & Torch Road and Wisdom Bridge Road, Hedong Road and the Poly-Xian Bridge Road & New Yue Road and Ao East Road, Hedong Lu and Poly-Xian Bridge Road & Torch Road and Poly-Xian Bridge Road, Hedong Road and Poly-Xian Bridge Road & Hedong Road and Ao Road, Hedong Road and Poly-Xian Bridge Road & Wisdom Bridge and Intellectual Island Road, Hedong Road and the Bridge Road & Torch Road, Torch Road and Hui Zhi Qiao Road & New Yue Road and Ao East Road, Torch Road and Hui Zhi Qiao Road & Torch Road and the Bridge Road, Torch Road and Hui Zhi Qiao Road & Wisdom Bridge and Intellectual Island Road, Torch Road and Wisdom Bridge Road & Hedong Road and the Bridge Road, Torch Road and Hui Zhi Qiao Road & Hedong Road and Ao Road, Hui Zhi Qiao and Intellectual Island Road & New Yue Road and Ao East Road, Wisdom Bridge and Intellectual Island Road & Torch Road and Poly-Xian Bridge Road, Wisdom Bridge and Intellectual Island Road & Torch Road and Wisdom Bridge Road, Wisdom Bridge and Intellectual Island Road & Hedong Road and Ao East Road, Wisdom Bridge and Intellectual Island Road & Hedong Rd and Poly-Xian Bridge Road,
Torch Road and Poly-Xian Bridge Road & New Yue Road and Ao East Road, Torch Road and Poly-Xian Bridge Road & Hedong Lu and Ao Road, Torch Road and Poly-Xian Bridge Road & Hedong Road and Poly-Xian Bridge Road, Torch Road and Poly-Xian Bridge Road & Wisdom Bridge and Intellectual Island Road, Torch Road and the wisdom of Bridge Road & Torch Road Lanru 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 Lanru 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 Lanru 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 Lanru 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 Lanru 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 Lanru 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 Lanru 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

Code:

Library (FPC)
data<-read.csv (' x.csv ')
df<-data[2:31]
set.seed (252964) 
(Kmeans <-Kmeans ( Na.omit (DF), 
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 ') # To write the results of a cluster to a file

2. R Language Association Rules

Data set format

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

Each column represents a property that indicates the occurrence of this property, and each row represents the number of records

The code is as follows:

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 algorithm */
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 (equivalent to inspect (Sort (frequentsets) [1:10])

</pre><pre code_snippet_id= "1620120" snippet_file_name= "blog_20160322_8_2841846" name= "code" class= "HTML" >/*eclat algorithm */

<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>
</div >