In elbamos/clusteringdatasets: Datasets useful for testing clustering algorithms

Clustering Datasets

This vignette provides a simple overview of the datasets included in the package.

knitr::opts_chunk$set(comment=NA, echo=FALSE, fig.width=6, fig.height=6)

Birch

library(clusteringdatasets)
data(birch1)
data(birch2)
data(birch3)
par(mfrow = c(1, 3), mar = c(0,0,1,0))
plot(birch1, cex = 0.0000005, main = "birch1", xlab = "", ylab = NULL, xaxt='n', yaxt = 'n')
plot(birch2, cex = 0.0000005, main = "birch2", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(birch3, cex = 0.0000005, main = "birch3", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')

S Sets

The S-sets are useful for testing how an algorithm handles cluster overlap.

data(s1)
data(s2)
data(s3)
data(s4)
par(mfrow = c(2, 2), mar = c(0,0,1,0))
plot(s1[, 1:2], cex = 0.0001, col = s1$labels, main = "s1", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(s2[, 1:2], cex = 0.0001, col = s2$labels, main = "s2", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(s3[, 1:2], cex = 0.0001, col = s3$labels, main = "s3", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(s4[, 1:2], cex = 0.0001, col = s4$labels, main = "s4", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')

A Sets

data(a1)
data(a2)
data(a3)
par(mfrow = c(1, 3), mar = c(0,0,1,0))
plot(a1[, 1:2], cex = 0.0001, col = s1$labels, main = "a1", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(a2[, 1:2], cex = 0.0001, col = s2$labels, main = "a2", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(a3[, 1:2], cex = 0.0001, col = s3$labels, main = "a3", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')

Shapesets

data("Aggregation")
data("spiral")
data("D31")
data(Compound)
data(pathbased)
data(jain)
data(flame)
data(R15)
par(mfrow = c(3, 3), mar = c(0,0,1,0))
plot(Aggregation[, 1:2], cex = 0.1, col = Aggregation$label, main = "Aggregation", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(spiral[, 1:2], cex = 0.1, col = spiral$label, main = "spiral", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(D31[, 1:2], cex = 0.1, col = D31$label, main = "D31", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(Compound[, 1:2], cex = 0.1, col = Compound$label, main = "Compound", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(pathbased[, 1:2], cex = 0.1, col = pathbased$label, main = "pathbased", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(jain[, 1:2], cex = 0.1, col = jain$label, main = "jain", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(flame[, 1:2], cex = 0.1, col = flame$label, main = "flame", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(R15[, 1:2], cex = 0.1, col = R15$label, main = "R15", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')

Chameleon

par(mfrow = c(2, 2), mar = c(0,0,1,0))
data("t48k")
data("t58k")
data("t710k")
data("t88k")
plot(t48k, cex = 0.001, main = "t48k", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(t58k, cex = 0.001, main = "t58k", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(t710k, cex = 0.001, main = "t710k", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(t88k, cex = 0.001, main = "t88k", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')

Neural Gas

names <- c("Circle"  ,  "Complex1"  , "Complex2" ,  "Complex3"    ,  "Complex4"     ,   "Discrete"  , "HiLoDensity" ,  "JumpingRectangle" ,
                     "MovingJumpingRectangle", "MovingRectangle", "Rectangle" ,  "RMouseRectangle"    ) # "Ring"
data(list = names)
par(mfrow = c(3, 5), mar = c(0, 0, 1, 0))
for (nm in names) {
    plot(eval(parse(text = nm)), cex = 0.01,  main = nm, xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
}

Non-Convex

names <- c("cross" , "d4"  ,   "face"  , "pie"  ,  "ring2"  , "sincos")
data(list = names)
par(mfrow = c(2, 3), mar = c(0, 0, 1, 0))
for (nm in names) {
    plot(eval(parse(text = nm)), cex = 0.1,  main = nm, xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
}

Locations

data("mopsifinland")
data("mopsijoensu")
par(mfrow = c(1, 2), mar = c(0,0,1,0))
plot(mopsifinland[, 1:2], cex = 0.01,  main = "mopsifinland", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
plot(mopsijoensu[, 1:2], cex = 0.05,  main = "mopsifinland", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')

High Dimensional Datasets

The package contains three sets of high-dimensional data. The visualizations below were made using my largeVis package to reduce each dataset to two dimensions, and the colors are the result of applying the hdbscan function within the package.

UCI Datasets

library(largeVis)
library(clusteringdatasets)
library(ggplot2)
data(glass)
data(wdbc)
data(breast)
data(yeast)
data(wine)
data(thyroid)
toproc <- list(glass, wdbc, breast, yeast, wine, thyroid)
vises <- list()
clusters <- list()
for (i in 1:length(toproc)) {
    dat <- t(scale(as.matrix(toproc[[i]])))
    if (ncol(dat) < 50000) vis <- largeVis(dat, K = 50, verbose = TRUE)
    else vis <- largeVis(dat, K = 100, verbose = TRUE)
    neighbors <- randomProjectionTreeSearch(dat, K = 50)
    edges <- buildEdgeMatrix(data = dat, neighbors = neighbors)
    print(str(edges))
    cluster <- hdbscan(edges = edges,neighbors = neighbors, K = 5, minPts = 10, verbose = TRUE)
    vises[[i]] <- vis
    clusters[[i]] <- cluster
}

library(ggplot2)
load(system.file("extdata/vises.Rda", package = "clusteringdatasets"))
load(system.file("extdata/clusters.Rda", package = "clusteringdatasets"))
names <- c("glass", "wdbc", "breast", "yeast", "wine", "thyroid")
par(mfrow = c(2, 3), mar = c(0,0,1,0))
for (i in 1:length(names)) {
    df <- data.frame(t(vises[[i]]$coords))
    colnames(df) <- c("x", "y")
    df$label <- clusters[[i]]$clusters
    if (length(unique(df$label)) > 1) {
        plot(df[, 1:2], cex = 0.005, col = df$label, main = names[i], xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
    } else {
        plot(df[, 1:2], cex = 0.005, col = df$label, main = names[i], xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n')
    }
}

KDDCUP04Bio

data("kddcup04bio")
library(largeVis)
load("./kddvis.Rda")
dat <- t(scale(as.matrix(kddcup04bio)))
vis <- largeVis(dat, K = 50, n_trees = 50, tree_threshold = 50, max_iter = 2, verbose = TRUE)

load(system.file("extdata/kdvis.Rda", package = "clusteringdatasets"))
par(mfrow = c(1, 1), mar = c(0,0,1,0))
plot(kdvis[, 1:2], cex = 0.0001, col = df$label, main = "kddcup04bio", xlab = NULL, ylab = NULL, xaxt='n',yaxt = 'n', 
         xlim = c(-20, 18), ylim = c(-20, 30))

elbamos/clusteringdatasets documentation built on May 16, 2019, 2:58 a.m.