K-Means Clustering of Dissimilarity Analysis Data Frames
Source:R/distantia_cluster_kmeans.R
distantia_cluster_kmeans.RdThis function combines the dissimilarity scores computed by distantia(), the K-means clustering method implemented in stats::kmeans(), and the clustering optimization method implemented in utils_cluster_hclust_optimizer() to help group together time series with similar features.
When clusters = NULL, the function utils_cluster_hclust_optimizer() is run underneath to perform a parallelized grid search to find the number of clusters maximizing the overall silhouette width of the clustering solution (see utils_cluster_silhouette()).
This function supports a parallelization setup via future::plan(), and progress bars provided by the package progressr.
Arguments
- df
(required, data frame) Output of
distantia(),distantia_ls(),distantia_dtw(), ordistantia_time_shift(). Default: NULL- clusters
(required, integer) Number of groups to generate. If NULL (default),
utils_cluster_kmeans_optimizer()is used to find the number of clusters that maximizes the mean silhouette width of the clustering solution (seeutils_cluster_silhouette()). Default: NULL- seed
(optional, integer) Random seed to be used during the K-means computation. Default: 1
Value
list:
cluster_object: kmeans object object for further analyses and custom plotting.clusters: integer, number of clusters.silhouette_width: mean silhouette width of the clustering solution.df: data frame with time series names, their cluster label, and their individual silhouette width scores.d: psi distance matrix used for clustering.optimization: only ifclusters = NULL, data frame with optimization results fromutils_cluster_hclust_optimizer().
See also
Other distantia_support:
distantia_aggregate(),
distantia_boxplot(),
distantia_cluster_hclust(),
distantia_matrix(),
distantia_model_frame(),
distantia_spatial(),
distantia_stats(),
distantia_time_shift(),
utils_block_size(),
utils_cluster_hclust_optimizer(),
utils_cluster_kmeans_optimizer(),
utils_cluster_silhouette()
Examples
#weekly covid prevalence in California
tsl <- tsl_initialize(
x = covid_prevalence,
name_column = "name",
time_column = "time"
)
#subset 10 elements to accelerate example execution
tsl <- tsl_subset(
tsl = tsl,
names = 1:10
)
if(interactive()){
#plotting first three time series
tsl_plot(
tsl = tsl[1:3],
guide_columns = 3
)
}
#dissimilarity analysis
distantia_df <- distantia(
tsl = tsl,
lock_step = TRUE
)
#hierarchical clustering
#automated number of clusters
distantia_kmeans <- distantia_cluster_kmeans(
df = distantia_df,
clusters = NULL
)
#names of the output object
names(distantia_kmeans)
#> [1] "cluster_object" "clusters" "silhouette_width" "df"
#> [5] "d" "optimization"
#kmeans object
distantia_kmeans$cluster_object
#> K-means clustering with 5 clusters of sizes 1, 2, 3, 2, 2
#>
#> Cluster means:
#> Alameda Imperial Fresno Butte Contra_Costa Kings Kern
#> 1 3.9600000 4.1732283 4.439863 4.233871 4.3809524 3.4817391 4.388889
#> 2 2.7233587 4.4888025 3.879361 1.163636 2.7502548 3.6589408 3.377273
#> 3 3.6957697 3.4930690 1.561499 3.380668 3.6431928 3.0464057 1.933922
#> 4 0.5810277 4.2236158 3.422312 2.848015 0.5810277 3.6756070 3.664084
#> 5 3.8752987 0.9587912 3.082938 4.016253 4.0239241 0.9587912 3.449647
#> Humboldt El_Dorado Los_Angeles
#> 1 0.000000 3.568627 4.647059
#> 2 3.901249 1.163636 4.261633
#> 3 4.491937 4.298177 1.907577
#> 4 4.170476 2.625598 3.922048
#> 5 3.827484 4.131490 3.276627
#>
#> Clustering vector:
#> Alameda Imperial Fresno Butte Contra_Costa Kings
#> 4 5 3 2 4 5
#> Kern Humboldt El_Dorado Los_Angeles
#> 3 1 2 3
#>
#> Within cluster sum of squares by cluster:
#> [1] 0.000000 7.154225 17.277466 1.538902 5.260079
#> (between_SS / total_SS = 80.4 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
#distance matrix used for clustering
distantia_kmeans$d
#> Alameda Imperial Fresno Butte Contra_Costa Kings Kern
#> Alameda 0.000000 4.119107 3.456869 2.962963 1.162055 3.631491 3.658065
#> Imperial 4.119107 0.000000 3.184685 4.408978 4.328125 1.917582 3.696145
#> Fresno 3.456869 3.184685 0.000000 3.324759 3.387755 2.981191 2.381766
#> Butte 2.962963 4.408978 3.324759 0.000000 2.733068 3.623529 2.811688
#> Contra_Costa 1.162055 4.328125 3.387755 2.733068 0.000000 3.719723 3.670103
#> Kings 3.631491 1.917582 2.981191 3.623529 3.719723 0.000000 3.203150
#> Kern 3.658065 3.696145 2.381766 2.811688 3.670103 3.203150 0.000000
#> Humboldt 3.960000 4.173228 4.439863 4.233871 4.380952 3.481739 4.388889
#> El_Dorado 2.483755 4.568627 4.433962 2.327273 2.767442 3.694352 3.942857
#> Los_Angeles 3.972376 3.598377 2.302730 4.005556 3.871720 2.954876 3.420000
#> Humboldt El_Dorado Los_Angeles
#> Alameda 3.960000 2.483755 3.972376
#> Imperial 4.173228 4.568627 3.598377
#> Fresno 4.439863 4.433962 2.302730
#> Butte 4.233871 2.327273 4.005556
#> Contra_Costa 4.380952 2.767442 3.871720
#> Kings 3.481739 3.694352 2.954876
#> Kern 4.388889 3.942857 3.420000
#> Humboldt 0.000000 3.568627 4.647059
#> El_Dorado 3.568627 0.000000 4.517711
#> Los_Angeles 4.647059 4.517711 0.000000
#> attr(,"distantia_args")
#> data frame with 0 columns and 0 rows
#> attr(,"type")
#> [1] "distantia_matrix"
#> attr(,"distance")
#> [1] "psi"
#number of clusters
distantia_kmeans$clusters
#> [1] 5
#clustering data frame
#group label in column "cluster"
distantia_kmeans$df
#> name cluster silhouette_width
#> 1 Alameda 4 0.5733007
#> 2 Imperial 5 0.4510322
#> 3 Fresno 3 0.2402546
#> 4 Butte 2 0.1828440
#> 5 Contra_Costa 4 0.5774736
#> 6 Kings 5 0.3705427
#> 7 Kern 3 0.1410575
#> 8 Humboldt 1 0.0000000
#> 9 El_Dorado 2 0.1136219
#> 10 Los_Angeles 3 0.1267346
#mean silhouette width of the clustering solution
distantia_kmeans$silhouette_width
#> [1] 0.2776862
#kmeans plot
# factoextra::fviz_cluster(
# object = distantia_kmeans$cluster_object,
# data = distantia_kmeans$d,
# repel = TRUE
# )