Perform k-means clustering for a different combinations of indices and distances.
Usage
clustInd_kmeans(
ind_data,
vars_combinations,
dist_vector = c("euclidean", "mahalanobis"),
n_cluster = 2,
init = "random",
true_labels = NULL,
n_cores = 1
)Arguments
- ind_data
Dataframe containing indices applied to the original data and its first and second derivatives. See generate_indices.
- vars_combinations
listcontaining one or more combinations of indices inind_data. If it is non-named, the names of the variables are set to vars1, ..., varsk, where k is the number of elements invars_combinations.- dist_vector
Atomic vector of distance metrics. The possible values are, "euclidean", "mahalanobis" or both.
- n_cluster
Number of clusters to create.
- init
Centroids initialization meathod. It can be "random" or "kmeanspp".
- true_labels
Vector of true labels for validation. (if it is not known true_labels is set to NULL)
- n_cores
Number of cores to do parallel computation. 1 by default, which mean no parallel execution.
Value
A list containing hierarchical clustering results for each configuration
A list containing kmeans clustering results for each configuration
Examples
vars1 <- c("dtaEI", "dtaMEI")
vars2 <- c("dtaHI", "dtaMHI")
data <- ehymet::sim_model_ex1()
data_ind <- generate_indices(data)
clustInd_kmeans(data_ind, list(vars1, vars2))
#> $kmeans_euclidean_dtaEIdtaMEI
#> $kmeans_euclidean_dtaEIdtaMEI$cluster
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#> 1 1 1 1 1 1 1 1 2 1 2 2 1 1 1 2 1 2 1 1
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
#> 2 1 2 2 2 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
#> 1 2 1 1 1 2 1 1 1 1 2 1 2 2 2 2 1 2 2 2
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
#> 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
#> 1 1 2 1 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2
#>
#> $kmeans_euclidean_dtaEIdtaMEI$internal_metrics
#> $kmeans_euclidean_dtaEIdtaMEI$internal_metrics$davies_bouldin
#> [1] 0.6538158
#>
#> $kmeans_euclidean_dtaEIdtaMEI$internal_metrics$dunn
#> [1] 0.03036249
#>
#> $kmeans_euclidean_dtaEIdtaMEI$internal_metrics$silhouette
#> [1] 0.5216865
#>
#> $kmeans_euclidean_dtaEIdtaMEI$internal_metrics$infomax
#> [1] 0.9988455
#>
#>
#> $kmeans_euclidean_dtaEIdtaMEI$time
#> [1] 0.002921581
#>
#>
#> $kmeans_euclidean_dtaHIdtaMHI
#> $kmeans_euclidean_dtaHIdtaMHI$cluster
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#> 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 2 1 1
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
#> 1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
#> 1 2 1 1 1 1 1 1 1 1 2 1 2 2 1 1 1 1 2 2
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
#> 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 1
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
#> 1 1 2 1 2 2 2 2 2 1 2 2 2 1 2 2 1 1 1 1
#>
#> $kmeans_euclidean_dtaHIdtaMHI$internal_metrics
#> $kmeans_euclidean_dtaHIdtaMHI$internal_metrics$davies_bouldin
#> [1] 0.6293133
#>
#> $kmeans_euclidean_dtaHIdtaMHI$internal_metrics$dunn
#> [1] 0.02940011
#>
#> $kmeans_euclidean_dtaHIdtaMHI$internal_metrics$silhouette
#> [1] 0.5466428
#>
#> $kmeans_euclidean_dtaHIdtaMHI$internal_metrics$infomax
#> [1] 0.9709506
#>
#>
#> $kmeans_euclidean_dtaHIdtaMHI$time
#> [1] 0.002859116
#>
#>
#> $kmeans_mahalanobis_dtaEIdtaMEI
#> $kmeans_mahalanobis_dtaEIdtaMEI$cluster
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#> 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 2
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
#> 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
#> 2 1 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 2 1 1
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
#> 1 1 2 2 1 1 1 1 1 2 1 2 1 1 1 1 1 1 2 2
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
#> 2 2 1 2 1 1 1 1 1 2 1 1 1 2 1 1 2 2 2 2
#>
#> $kmeans_mahalanobis_dtaEIdtaMEI$internal_metrics
#> $kmeans_mahalanobis_dtaEIdtaMEI$internal_metrics$davies_bouldin
#> [1] 0.6120092
#>
#> $kmeans_mahalanobis_dtaEIdtaMEI$internal_metrics$dunn
#> [1] 0.02472821
#>
#> $kmeans_mahalanobis_dtaEIdtaMEI$internal_metrics$silhouette
#> [1] 0.5518717
#>
#> $kmeans_mahalanobis_dtaEIdtaMEI$internal_metrics$infomax
#> [1] 0.958042
#>
#>
#> $kmeans_mahalanobis_dtaEIdtaMEI$time
#> [1] 0.002937555
#>
#>
#> $kmeans_mahalanobis_dtaHIdtaMHI
#> $kmeans_mahalanobis_dtaHIdtaMHI$cluster
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
#> 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 2 1
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
#> 2 1 1 1 1 2 2 2 1 1 2 1 2 2 2 2 1 1 1 1
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
#> 1 1 1 1 2 2 2 2 2 1 1 2 1 1 1 2 1 1 1 1
#>
#> $kmeans_mahalanobis_dtaHIdtaMHI$internal_metrics
#> $kmeans_mahalanobis_dtaHIdtaMHI$internal_metrics$davies_bouldin
#> [1] 0.5716305
#>
#> $kmeans_mahalanobis_dtaHIdtaMHI$internal_metrics$dunn
#> [1] 0.04194988
#>
#> $kmeans_mahalanobis_dtaHIdtaMHI$internal_metrics$silhouette
#> [1] 0.5695492
#>
#> $kmeans_mahalanobis_dtaHIdtaMHI$internal_metrics$infomax
#> [1] 0.7014715
#>
#>
#> $kmeans_mahalanobis_dtaHIdtaMHI$time
#> [1] 0.002941132
#>
#>