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# Datamining R 5th

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### Datamining R 5th

1. 1. R: ( ) sesejun@is.ocha.ac.jp 2009/12/10
2. 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. 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)
4. 4. > 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> :
5. 5. 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