124 lines
3.2 KiB
Plaintext
124 lines
3.2 KiB
Plaintext
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R version 3.5.0 alpha (2018-03-28 r74481)
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Copyright (C) 2018 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu (64-bit)
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> library(cluster)
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>
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> ### clusplot() & pam() RESULT checking ...
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>
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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)$clustering
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+ print(clusplot(votes.diss, votes.clus, diss = TRUE, shade = TRUE))
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+ }
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$Distances
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[,1] [,2]
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[1,] 0.0000 154.8601
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[2,] 154.8601 0.0000
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$Shading
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[1] 22.12103 20.87897
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$Distances
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[,1] [,2] [,3]
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[1,] 0.0000 NA 140.8008
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[2,] NA 0 NA
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[3,] 140.8008 NA 0.0000
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$Shading
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[1] 25.602967 5.292663 15.104370
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$Distances
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[,1] [,2] [,3] [,4]
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[1,] 0.0000 NA 117.2287 280.7259
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[2,] NA 0 NA NA
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[3,] 117.2287 NA 0.0000 NA
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[4,] 280.7259 NA NA 0.0000
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$Shading
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[1] 15.431339 3.980743 10.145454 19.442464
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>
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> ## plotting iris (dataframe) in a 2-dimensional plot and partitioning
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> ## into 3 clusters.
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> data(iris)
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> iris.x <- iris[, 1:4]
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>
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> for(k in 2:5)
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+ print(clusplot(iris.x, pam(iris.x, k)$clustering, diss = FALSE))
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$Distances
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[,1] [,2]
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[1,] 0.0000000 0.5452161
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[2,] 0.5452161 0.0000000
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$Shading
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[1] 18.93861 24.06139
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$Distances
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[,1] [,2] [,3]
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[1,] 0.000000 1.433071 2.851715
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[2,] 1.433071 0.000000 NA
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[3,] 2.851715 NA 0.000000
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$Shading
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[1] 18.987588 17.166940 9.845472
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$Distances
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[,1] [,2] [,3] [,4]
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[1,] 0.000000 2.24157 1.6340329 3.0945887
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[2,] 2.241570 0.00000 NA NA
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[3,] 1.634033 NA 0.0000000 0.9461858
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[4,] 3.094589 NA 0.9461858 0.0000000
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$Shading
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[1] 12.881766 14.912379 13.652381 7.553474
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$Distances
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[,1] [,2] [,3] [,4] [,5]
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[1,] 0.000000 1.9899160 1.552387 3.11516148 3.96536391
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[2,] 1.989916 0.0000000 NA NA 0.94713417
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[3,] 1.552387 NA 0.000000 1.17348334 2.22769309
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[4,] 3.115161 NA 1.173483 0.00000000 0.04539385
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[5,] 3.965364 0.9471342 2.227693 0.04539385 0.00000000
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$Shading
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[1] 10.369738 11.560147 10.088431 14.857590 5.124093
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>
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>
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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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$Distances
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[,1] [,2]
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[1,] 0.000000 5.516876
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[2,] 5.516876 0.000000
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$Shading
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[1] 20.18314 22.81686
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>
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> clusplot(px2, labels = 2, col.p = 1 + px2$clustering)
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>
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> proc.time()
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user system elapsed
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0.320 0.070 0.449
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