distantia (version 2.0.0)

distantia_cluster_hclust: Hierarchical Clustering of Dissimilarity Analysis Data Frames

Description

This function combines the dissimilarity scores computed by distantia(), the agglomerative clustering methods provided by stats::hclust(), 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()). When method = NULL as well, the optimization also includes all methods available in stats::hclust() in the grid search.

This function supports a parallelization setup via future::plan(), and progress bars provided by the package progressr.

Usage

distantia_cluster_hclust(df = NULL, clusters = NULL, method = "complete")

Value

list:

• cluster_object: hclust 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 if clusters = NULL, data frame with optimization results from utils_cluster_hclust_optimizer().

Arguments

df

(required, data frame) Output of distantia(), distantia_ls(), distantia_dtw(), or distantia_time_delay(). 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 (see utils_cluster_silhouette()). Default: NULL

method

(optional, character string) Argument of stats::hclust() defining the agglomerative method. One of: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC). Unambiguous abbreviations are accepted as well. If NULL (default), utils_cluster_hclust_optimizer() finds the optimal method. Default: "complete".

See Also

Other distantia_support: distantia_aggregate(), distantia_boxplot(), distantia_cluster_kmeans(), distantia_matrix(), distantia_model_frame(), distantia_spatial(), distantia_stats(), distantia_time_delay(), 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
#automated method selection
distantia_clust <- distantia_cluster_hclust(
  df = distantia_df,
  clusters = NULL,
  method = NULL
)

#names of the output object
names(distantia_clust)

#cluster object
distantia_clust$cluster_object

#distance matrix used for clustering
distantia_clust$d

#number of clusters
distantia_clust$clusters

#clustering data frame
#group label in column "cluster"
#negatives in column "silhouette_width" higlight anomalous cluster assignation
distantia_clust$df

#mean silhouette width of the clustering solution
distantia_clust$silhouette_width

#plot
if(interactive()){

  dev.off()

  clust <- distantia_clust$cluster_object
  k <- distantia_clust$clusters

  #tree plot
  plot(
    x = clust,
    hang = -1
  )

  #highlight groups
  stats::rect.hclust(
    tree = clust,
    k = k,
    cluster = stats::cutree(
      tree = clust,
      k = k
    )
  )

}