The goal of clust431, created by Nils, Ryuhei, and Scott, is to provide
R users with a K-means algorithm to assign k-clusters to any set of
numerical data. The k-means() function has the ability to use a
K-means++ algorithm to assign the original k-centroids, or use random
assignment if pca == FALSE.
clust431 also provides R users with the hier_clust() function, which
outputs cluster assignment based on Hierarchical Agglomerative
Clustering. By default, the distance measurement used to calculate
clustering assignments is method = "euclidean", but the methods
"euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"
can be input.
You can install the released version of clust431 from CRAN with:
install.packages("clust431")library(clust431)- Users are provided with the observations’ cluster assignments, distance to their respective cluster centroid, centroid coordinates of each respective cluster centroid, number of iterations to achieve the same clustering assignment, and the total sum of squares of the observations to the mean centroid of that data set.
k_means(iris, 3, pca = FALSE)
#> $Clusterings
#> Sepal.Length Sepal.Width Petal.Length Petal.Width clusters
#> 1 5.1 3.5 1.4 0.2 1
#> 2 4.9 3.0 1.4 0.2 1
#> 3 4.7 3.2 1.3 0.2 1
#> 4 4.6 3.1 1.5 0.2 1
#> 5 5.0 3.6 1.4 0.2 1
#> 6 5.4 3.9 1.7 0.4 2
#> 7 4.6 3.4 1.4 0.3 1
#> 8 5.0 3.4 1.5 0.2 1
#> 9 4.4 2.9 1.4 0.2 1
#> 10 4.9 3.1 1.5 0.1 1
#> 11 5.4 3.7 1.5 0.2 2
#> 12 4.8 3.4 1.6 0.2 1
#> 13 4.8 3.0 1.4 0.1 1
#> 14 4.3 3.0 1.1 0.1 1
#> 15 5.8 4.0 1.2 0.2 2
#> 16 5.7 4.4 1.5 0.4 2
#> 17 5.4 3.9 1.3 0.4 2
#> 18 5.1 3.5 1.4 0.3 1
#> 19 5.7 3.8 1.7 0.3 2
#> 20 5.1 3.8 1.5 0.3 2
#> 21 5.4 3.4 1.7 0.2 1
#> 22 5.1 3.7 1.5 0.4 2
#> 23 4.6 3.6 1.0 0.2 1
#> 24 5.1 3.3 1.7 0.5 2
#> 25 4.8 3.4 1.9 0.2 1
#> 26 5.0 3.0 1.6 0.2 1
#> 27 5.0 3.4 1.6 0.4 1
#> 28 5.2 3.5 1.5 0.2 1
#> 29 5.2 3.4 1.4 0.2 1
#> 30 4.7 3.2 1.6 0.2 1
#> 31 4.8 3.1 1.6 0.2 1
#> 32 5.4 3.4 1.5 0.4 1
#> 33 5.2 4.1 1.5 0.1 2
#> 34 5.5 4.2 1.4 0.2 2
#> 35 4.9 3.1 1.5 0.2 1
#> 36 5.0 3.2 1.2 0.2 1
#> 37 5.5 3.5 1.3 0.2 1
#> 38 4.9 3.6 1.4 0.1 1
#> 39 4.4 3.0 1.3 0.2 1
#> 40 5.1 3.4 1.5 0.2 1
#> 41 5.0 3.5 1.3 0.3 1
#> 42 4.5 2.3 1.3 0.3 1
#> 43 4.4 3.2 1.3 0.2 1
#> 44 5.0 3.5 1.6 0.6 2
#> 45 5.1 3.8 1.9 0.4 2
#> 46 4.8 3.0 1.4 0.3 1
#> 47 5.1 3.8 1.6 0.2 2
#> 48 4.6 3.2 1.4 0.2 1
#> 49 5.3 3.7 1.5 0.2 2
#> 50 5.0 3.3 1.4 0.2 1
#> 51 7.0 3.2 4.7 1.4 3
#> 52 6.4 3.2 4.5 1.5 3
#> 53 6.9 3.1 4.9 1.5 3
#> 54 5.5 2.3 4.0 1.3 2
#> 55 6.5 2.8 4.6 1.5 3
#> 56 5.7 2.8 4.5 1.3 3
#> 57 6.3 3.3 4.7 1.6 3
#> 58 4.9 2.4 3.3 1.0 2
#> 59 6.6 2.9 4.6 1.3 3
#> 60 5.2 2.7 3.9 1.4 2
#> 61 5.0 2.0 3.5 1.0 2
#> 62 5.9 3.0 4.2 1.5 2
#> 63 6.0 2.2 4.0 1.0 2
#> 64 6.1 2.9 4.7 1.4 3
#> 65 5.6 2.9 3.6 1.3 2
#> 66 6.7 3.1 4.4 1.4 3
#> 67 5.6 3.0 4.5 1.5 3
#> 68 5.8 2.7 4.1 1.0 2
#> 69 6.2 2.2 4.5 1.5 3
#> 70 5.6 2.5 3.9 1.1 2
#> 71 5.9 3.2 4.8 1.8 3
#> 72 6.1 2.8 4.0 1.3 2
#> 73 6.3 2.5 4.9 1.5 3
#> 74 6.1 2.8 4.7 1.2 3
#> 75 6.4 2.9 4.3 1.3 3
#> 76 6.6 3.0 4.4 1.4 3
#> 77 6.8 2.8 4.8 1.4 3
#> 78 6.7 3.0 5.0 1.7 3
#> 79 6.0 2.9 4.5 1.5 3
#> 80 5.7 2.6 3.5 1.0 2
#> 81 5.5 2.4 3.8 1.1 2
#> 82 5.5 2.4 3.7 1.0 2
#> 83 5.8 2.7 3.9 1.2 2
#> 84 6.0 2.7 5.1 1.6 3
#> 85 5.4 3.0 4.5 1.5 3
#> 86 6.0 3.4 4.5 1.6 3
#> 87 6.7 3.1 4.7 1.5 3
#> 88 6.3 2.3 4.4 1.3 3
#> 89 5.6 3.0 4.1 1.3 2
#> 90 5.5 2.5 4.0 1.3 2
#> 91 5.5 2.6 4.4 1.2 2
#> 92 6.1 3.0 4.6 1.4 3
#> 93 5.8 2.6 4.0 1.2 2
#> 94 5.0 2.3 3.3 1.0 2
#> 95 5.6 2.7 4.2 1.3 2
#> 96 5.7 3.0 4.2 1.2 2
#> 97 5.7 2.9 4.2 1.3 2
#> 98 6.2 2.9 4.3 1.3 3
#> 99 5.1 2.5 3.0 1.1 2
#> 100 5.7 2.8 4.1 1.3 2
#> 101 6.3 3.3 6.0 2.5 3
#> 102 5.8 2.7 5.1 1.9 3
#> 103 7.1 3.0 5.9 2.1 3
#> 104 6.3 2.9 5.6 1.8 3
#> 105 6.5 3.0 5.8 2.2 3
#> 106 7.6 3.0 6.6 2.1 3
#> 107 4.9 2.5 4.5 1.7 2
#> 108 7.3 2.9 6.3 1.8 3
#> 109 6.7 2.5 5.8 1.8 3
#> 110 7.2 3.6 6.1 2.5 3
#> 111 6.5 3.2 5.1 2.0 3
#> 112 6.4 2.7 5.3 1.9 3
#> 113 6.8 3.0 5.5 2.1 3
#> 114 5.7 2.5 5.0 2.0 3
#> 115 5.8 2.8 5.1 2.4 3
#> 116 6.4 3.2 5.3 2.3 3
#> 117 6.5 3.0 5.5 1.8 3
#> 118 7.7 3.8 6.7 2.2 3
#> 119 7.7 2.6 6.9 2.3 3
#> 120 6.0 2.2 5.0 1.5 3
#> 121 6.9 3.2 5.7 2.3 3
#> 122 5.6 2.8 4.9 2.0 3
#> 123 7.7 2.8 6.7 2.0 3
#> 124 6.3 2.7 4.9 1.8 3
#> 125 6.7 3.3 5.7 2.1 3
#> 126 7.2 3.2 6.0 1.8 3
#> 127 6.2 2.8 4.8 1.8 3
#> 128 6.1 3.0 4.9 1.8 3
#> 129 6.4 2.8 5.6 2.1 3
#> 130 7.2 3.0 5.8 1.6 3
#> 131 7.4 2.8 6.1 1.9 3
#> 132 7.9 3.8 6.4 2.0 3
#> 133 6.4 2.8 5.6 2.2 3
#> 134 6.3 2.8 5.1 1.5 3
#> 135 6.1 2.6 5.6 1.4 3
#> 136 7.7 3.0 6.1 2.3 3
#> 137 6.3 3.4 5.6 2.4 3
#> 138 6.4 3.1 5.5 1.8 3
#> 139 6.0 3.0 4.8 1.8 3
#> 140 6.9 3.1 5.4 2.1 3
#> 141 6.7 3.1 5.6 2.4 3
#> 142 6.9 3.1 5.1 2.3 3
#> 143 5.8 2.7 5.1 1.9 3
#> 144 6.8 3.2 5.9 2.3 3
#> 145 6.7 3.3 5.7 2.5 3
#> 146 6.7 3.0 5.2 2.3 3
#> 147 6.3 2.5 5.0 1.9 3
#> 148 6.5 3.0 5.2 2.0 3
#> 149 6.2 3.4 5.4 2.3 3
#> 150 5.9 3.0 5.1 1.8 3
#> smallest_distances
#> 1 0.0000000
#> 2 0.5385165
#> 3 0.5099020
#> 4 0.6480741
#> 5 0.1414214
#> 6 0.4123106
#> 7 0.5196152
#> 8 0.1732051
#> 9 0.9219544
#> 10 0.4690416
#> 11 0.3605551
#> 12 0.3741657
#> 13 0.5916080
#> 14 0.9949874
#> 15 0.8426150
#> 16 0.9219544
#> 17 0.4123106
#> 18 0.1000000
#> 19 0.6480741
#> 20 0.1414214
#> 21 0.4358899
#> 22 0.0000000
#> 23 0.6480741
#> 24 0.4582576
#> 25 0.5916080
#> 26 0.5477226
#> 27 0.3162278
#> 28 0.1414214
#> 29 0.1414214
#> 30 0.5385165
#> 31 0.5385165
#> 32 0.3872983
#> 33 0.5099020
#> 34 0.6782330
#> 35 0.4582576
#> 36 0.3741657
#> 37 0.4123106
#> 38 0.2449490
#> 39 0.8660254
#> 40 0.1414214
#> 41 0.1732051
#> 42 1.3490738
#> 43 0.7681146
#> 44 0.3162278
#> 45 0.4123106
#> 46 0.5916080
#> 47 0.2449490
#> 48 0.5830952
#> 49 0.2828427
#> 50 0.2236068
#> 51 2.1023796
#> 52 2.5436195
#> 53 1.9974984
#> 54 3.0298515
#> 55 2.5396850
#> 56 3.1527766
#> 57 2.4207437
#> 58 2.3086793
#> 59 2.4959968
#> 60 2.7874720
#> 61 2.6944387
#> 62 3.1032241
#> 63 3.1096624
#> 64 2.7018512
#> 65 2.4718414
#> 66 2.5079872
#> 67 3.1288976
#> 68 2.9342802
#> 69 3.0512293
#> 70 2.8178006
#> 71 2.6381812
#> 72 2.9782545
#> 73 2.5980762
#> 74 2.7874720
#> 75 2.8213472
#> 76 2.5865034
#> 77 2.2649503
#> 78 2.0322401
#> 79 2.8774989
#> 80 2.4351591
#> 81 2.7622455
#> 82 2.6551836
#> 83 2.8089144
#> 84 2.5826343
#> 85 3.2787193
#> 86 2.7459060
#> 87 2.2516660
#> 88 3.0495901
#> 89 2.8827071
#> 90 2.9427878
#> 91 3.2280025
#> 92 2.7349589
#> 93 2.9308702
#> 94 2.3600847
#> 95 3.0577770
#> 96 2.9631065
#> 97 3.0166206
#> 98 2.9325757
#> 99 2.0445048
#> 100 2.9563491
#> 101 1.7944358
#> 102 2.7055499
#> 103 1.2409674
#> 104 2.0124612
#> 105 1.7320508
#> 106 0.8831761
#> 107 3.4885527
#> 108 1.1045361
#> 109 1.8788294
#> 110 0.9327379
#> 111 2.0024984
#> 112 2.1633308
#> 113 1.6340135
#> 114 2.9137605
#> 115 2.6944387
#> 116 1.9773720
#> 117 1.8574176
#> 118 0.4123106
#> 119 1.3490738
#> 120 2.8948230
#> 121 1.3928388
#> 122 2.9223278
#> 123 1.0630146
#> 124 2.4617067
#> 125 1.4798649
#> 126 1.0246951
#> 127 2.5475478
#> 128 2.4839485
#> 129 1.9748418
#> 130 1.2845233
#> 131 1.1618950
#> 132 0.0000000
#> 133 1.9824228
#> 134 2.3452079
#> 135 2.3832751
#> 136 0.9273618
#> 137 1.8761663
#> 138 1.8947295
#> 139 2.6172505
#> 140 1.5811388
#> 141 1.6522712
#> 142 1.8083141
#> 143 2.7055499
#> 144 1.3820275
#> 145 1.5588457
#> 146 1.9000000
#> 147 2.4939928
#> 148 2.0099751
#> 149 2.0346990
#> 150 2.5238859
#>
#> $`Cluster Means`
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 5.1 3.5 1.4 0.2
#> 22 5.1 3.7 1.5 0.4
#> 132 7.9 3.8 6.4 2.0
#>
#> $Iterations
#> [1] 1
#>
#> $`Total Sum of Squares`
#> [1] 291.6103results <- k_means(iris, 3, pca = TRUE)
plot_clusterings(results$Clusterings)
In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub!
hier_clust(iris)
#> Warning in dist(data, method = method, diag = TRUE, upper = TRUE): NAs
#> introduced by coercion
#> [1] 42 60 133 137 26 51 74 125 76 109 138 140 102 143 12 37 25 27
#> [19] 17 62 105 13 47 139 92 120 110 111 73 124 84 94 44 68 20 93
#> [37] 87 113 56 71 142 148 149 34 66 112 135 96 65 101 100 141 32 97
#> [55] 48 50 147 6 43 1 19 98 118 7 24 2 53 122 145 30 72 35
#> [73] 49 88 115 130 8 40 106 134 80 83 126 146 39 41 5 57 70 103
#> [91] 127 150 4 16 136 95 107 22 45 89 90 104 129 15 29 81 85 21
#> [109] 31 36 38 114 119 67 144 78 33 63 61 69 18 64 46 54 75 99
#> [127] 77 108 131 116 10 11 123 117 121 3 58 9 28 82 86 14 52 23
#> [145] 55 128 132 59 79 91