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Calculate Clustering Quality Metrics

Source: R/semantic_analysis.R
calculate_clustering_metrics.Rd

Calculates common clustering evaluation metrics including Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index.

Usage

calculate_clustering_metrics(
  clusters,
  data_matrix,
  dist_matrix = NULL,
  metrics = "all"
)

Arguments

clusters

Integer vector of cluster assignments

data_matrix

Numeric matrix of data points (rows = observations, cols = features)

dist_matrix

Optional distance matrix. If NULL, computed from data_matrix

metrics

Character vector of metrics to calculate. Options: "silhouette", "davies_bouldin", "calinski_harabasz", or "all" (default)

Value

A named list containing:

silhouette

Silhouette score (-1 to 1, higher is better)

davies_bouldin

Davies-Bouldin index (lower is better)

calinski_harabasz

Calinski-Harabasz index (higher is better)

n_clusters

Number of clusters

cluster_sizes

Table of cluster sizes

Details

  • Silhouette Score: Measures how similar an object is to its own cluster compared to other clusters. Range: -1 to 1, higher is better.

  • Davies-Bouldin Index: Average similarity between each cluster and its most similar cluster. Lower values indicate better clustering.

  • Calinski-Harabasz Index: Ratio of between-cluster to within-cluster variance. Higher values indicate better-defined clusters.

Examples

# \donttest{
abstracts <- TextAnalysisR::SpecialEduTech$abstract[1:20]
term_matrix <- as.matrix(quanteda::dfm(quanteda::tokens(abstracts)))
kmeans_result <- stats::kmeans(term_matrix, centers = 2)
metrics <- calculate_clustering_metrics(kmeans_result$cluster, term_matrix)
print(metrics)
#> $n_clusters
#> [1] 2
#> 
#> $cluster_sizes
#> clusters
#>  1  2 
#> 14  6 
#> 
#> $silhouette
#> [1] 0.4675644
#> 
#> $davies_bouldin
#> [1] 1.024449
#> 
#> $calinski_harabasz
#> [1] 16.50688
#> 
# }