機構セミナー@2015年1月15日 に載せるグラフを生成するRスクリプト

compare_nonhierarchical_clustering.r

##
## Compare methods for nonhierarchical clustering
##
## Please install following libraries in advance
##    * LICORS
##    * apcluster
##
## @athor kyon
## @created Jan. 2 2015
##

#
# generate 4-calsses dataset
#
gen.dataset <- function(sd) {
    n <- 25    # the number of data for each label
    p1 <- cbind(rnorm(n=n, mean=1, sd=sd),rnorm(n=n, mean=1, sd=sd))
    p2 <- cbind(rnorm(n=n, mean=10, sd=sd),rnorm(n=n, mean=1, sd=sd))
    p3 <- cbind(rnorm(n=n, mean=1, sd=sd),rnorm(n=n, mean=10, sd=sd))
    p4 <- cbind(rnorm(n=n, mean=10, sd=sd),rnorm(n=n, mean=10, sd=sd))
    label <- as.factor(c(rep(1,n), rep(2,n), rep(3,n), rep(4,n)))
    d <- as.data.frame(cbind(rbind(p1, p2, p3, p4), label))
    colnames(d) <- c('x', 'y', 'label')
    return(d)
}

#
# run clustering and plot result
#
compare.plot <- function(d, label) {
    #init
    par(mfrow=c(2, 3))
    n_cluster <- length(levels(factor(label)))

    #answer
    plot(d, pch=as.integer(label), col=as.integer(label), main='Answer')

    #k-means
    d.km <- kmeans(d, n_cluster)
    plot(d, pch=d.km$cluster, col=d.km$cluster, main='k-means')

    #k-means++
    library(LICORS)
    d.kmpp <- kmeanspp(d, n_cluster)
    plot(d, pch=d.kmpp$cluster, col=d.kmpp$cluster, main='k-means++')

    #x-means
    source('http://www.rd.dnc.ac.jp/~tunenori/src/xmeans.prog')
    d.xm <- xmeans(d)
    plot(d, pch=d.xm$cluster, col=d.xm$cluster, main='x-means')

    #Affinity Propagation
    require('apcluster')
    d.s <- negDistMat(d, r=2)
    d.ap <- apcluster(d.s)
    plot(d, pch=as.integer(factor(labels(d.ap))), col=as.integer(factor(labels(d.ap))), main='Affinity Propagation')
}

#init pdf
pdf('clustering.pdf')

#artificial dataset
for (sd in seq(1, 3, 0.5)) {
    d <- gen.dataset(sd=sd)
    compare.plot(d[,1:2], d$label)
}

#iris dataset
## compare.plot(iris[,1:2], iris$Species)
compare.plot(iris[,2:3], iris$Species)

#finalize
dev.off()