(1)凝集型階層
> library(cluster)
> cluster.agnes <- agnes="" mysql.data="" span="" stand="T)">> plot(cluster.agnes, which = 2, main = "", xlab = "", ylab = "")->
(2) 区分型階層
> cluster.diana <- diana="" mysql.data="" span="" stand="T)">> plot(cluster.diana, which = 2, main = "", xlab = "", ylab = "")->
(3) k-means法
> cluster.kmeans = kmeans(dist(mysql.data), 6) > cluster.kmeans$cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 6 1 3 3 3 3 3 3 2 4 4 4 4 4 4 4 4 4 4 4 4 4 2 6
> cluster.kmeans$size [1] 6 2 6 13 11 2
> cluster.kmeans$withinss [1] 57677.519 5325.897 82998.094 383242.093 83159.601 102326.957
> clusplot(mysql.data, cluster.kmeans$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
(4) 階層クラスタリング
> rect.hclust(hclust(dist(mysql.data), method="ward.D"), k = 5, border = "red")
[Cluster.R]
参考
http://catcher-in-the-tech.net/2035/
http://d.hatena.ne.jp/maito610/20120810/p1
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