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Datamining R 5th
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Datamining R 5th
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