1. R:
( )
sesejun@is.ocha.ac.jp
2009/12/10

2. k-means
> usps<-read.table("usps/usps_cluster.csv", header=T, sep=",")
> usps.sub<-usps[3:length(usps)]
> rownames(usps.sub)<-usps$ImageName
> usps.kmeans<-kmeans(usps.sub, 3, iter.max=100)
> usps.kmeans$size
[1] 5 2 3
> usps.kmeans$cluster
[1] 2 3 3 1 1 2 3 1 1 1
> usps.kmeans

3. > usps.dist<-dist(usps.sub, method="euclidean")
> usps.dist
img_0_00_00 img_1_00_00 img_2_00_00 img_3_00_00
img_1_00_00 2517.392
img_2_00_00 2172.201 2204.662
img_3_00_00 2073.739 2128.806 2225.389
img_4_00_00 2239.165 1915.576 2220.492 1928.101
img_5_00_00 1981.039 2472.299 2179.280 2400.684
...
> usps.hclust<-hclust(usps.dist,method="single")
> plot(usps.hclust)

5. > library(cluster)
> usps.div<-diana(usps.sub, metric="euclidian",stand=TRUE)
> print(usps.div)
Merge:
[,1] [,2]
[1,] -8 -10
[2,] -2 -7
[3,] -4 -5
[4,] 1 -9
...
> plot(usps.div)
<Return> :
<Return> :

7. 1. k-means usps_cluster_large.tab k
k 5
• usps_cluster_large.tab 0 9 5 50
2. DIANA
usps_cluster_large.tab
• 1,2
3.
• 1 29