アイデアプログラムのTestngファイルのVBS時限実行

#loadcluster
library(cluster)
#Read in the data to be clustered
a<-read.csv("testdata.csv",header=TRUE)
str(a)
'data.frame': 17 obs. of 3 variables:
 $ Node classification: Factor w/ 17 levels "store1","store10",... : 1 10 11 12 13 14 15 16 17 2 ...
 $ Sales amount: num 29.41 9.72 6.15 5.72 5.57 ...
 $ Number of customers: num 141.3 84.7 95 98.5 58.3 ...
# View the first 4 rows of data
> head(a,n=4)
  Network classification Sales amount Number of customers
1 store1 29.408587 141.33560
2 store2 9.719984 84.67674
3 store3 6.154773 95.00602
4 store4 5.721835 98.48247
# Divide the first indicator into 3 categories
res1<-kmeans(a$ sales amount,3)
# divide the second indicator into 3 categories
res2<-kmeans(a$ number of customers,3)
#Cluster the results by column in the dataset
res3<-cbind(a,res1$cluster,res2$cluster)
#View the first 4 rows of data
> head(res3,n=4)
  Network classification Sales amount Number of customers res1$cluster res2$cluster
1 store1 29.408587 141.33560 3 1
2 store2 9.719984 84.67674 1 2
3 store3 6.154773 95.00602 1 2
4 store4 5.721835 98.48247 1 2
#Cluster centers for both metrics
> res1$centers
       [,1]
1 5.519207
2 2.566558
3 29.408587
> res2$centers
       [,1]
1 123.11407
2 86.39321
3 64.49325
#Export clustering results
write.csv(res3,file='res3.csv')