This package provides R binding to a cpp implementation of the kmeans++ algorithm.
You can install the released version of tglkmeans using the following command:
install.packages("tglkmeans")Or install the development version using:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("tanaylab/tglkmeans")library(tglkmeans)Create 5 clusters normally distributed around 1 to 5, with sd of 0.3:
data <- rbind(
matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 3, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 5, sd = 0.3), ncol = 2)
)
colnames(data) <- c("x", "y")
head(data)
#> x y
#> [1,] 0.8247609 0.8524763
#> [2,] 0.8617387 1.2519285
#> [3,] 1.0966104 1.5035216
#> [4,] 0.6755113 0.7076129
#> [5,] 1.3306283 0.7485884
#> [6,] 1.0784311 1.2460204Cluster using kmeans++:
km <- TGL_kmeans(data, k = 5, id_column = FALSE)
km
#> $cluster
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#> 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
#> 4 4 4 4 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
#> 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
#> 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
#> 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
#> 1 1 1 1 1 1 1 1 3 1 2 2 2 2 2 2 1 2 2 2
#> 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
#> 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
#> 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 2 2 2
#> 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220
#> 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
#> 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
#> 241 242 243 244 245 246 247 248 249 250
#> 5 5 5 5 5 5 5 5 5 5
#>
#> $centers
#> x y
#> [1,] 2.968139 2.950851
#> [2,] 4.021329 3.991103
#> [3,] 1.965148 1.992092
#> [4,] 0.997430 1.012817
#> [5,] 4.952484 4.973394
#>
#> $size
#> 1 2 3 4 5
#> 52 48 50 49 51Plot the results:
plot(data, col = km$cluster)
points(km$centers, pch = 8, cex = 2)
Please refer to the package vignettes for usage and workflow, or look at the usage section in the site.
browseVignettes("usage")