30 lines
852 B
R
30 lines
852 B
R
library(cluster)
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### clusplot() & pam() RESULT checking ...
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## plotting votes.diss(dissimilarity) in a bivariate plot and
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## partitioning into 2 clusters
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data(votes.repub)
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votes.diss <- daisy(votes.repub)
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for(k in 2:4) {
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votes.clus <- pam(votes.diss, k, diss = TRUE)$clustering
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print(clusplot(votes.diss, votes.clus, diss = TRUE, shade = TRUE))
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}
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## plotting iris (dataframe) in a 2-dimensional plot and partitioning
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## into 3 clusters.
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data(iris)
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iris.x <- iris[, 1:4]
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for(k in 2:5)
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print(clusplot(iris.x, pam(iris.x, k)$clustering, diss = FALSE))
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.Random.seed <- c(0L,rep(7654L,3))
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## generate 25 objects, divided into 2 clusters.
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x <- rbind(cbind(rnorm(10,0,0.5), rnorm(10,0,0.5)),
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cbind(rnorm(15,5,0.5), rnorm(15,5,0.5)))
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print.default(clusplot(px2 <- pam(x, 2)))
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clusplot(px2, labels = 2, col.p = 1 + px2$clustering)
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