Calculate Silhouette Width for Soft Clustering Algorithms

Computes silhouette widths for soft clustering results by interpreting cluster membership probabilities (or their transformations) as proximity measures. Although originally designed for evaluating clustering quality within a method, this adaptation allows heuristic comparison across soft clustering algorithms using average silhouette widths.

Usage

softSilhouette(
  prob_matrix,
  prob_type = c("pp", "nlpp", "pd"),
  method = c("pac", "medoid"),
  average = c("crisp", "fuzzy"),
  a = 2,
  print.summary = FALSE,
  clust_fun = NULL,
  ...
)

Arguments

prob_matrix

A numeric matrix where rows represent observations and columns represent cluster membership probabilities (or transformed probabilities, depending on prob_type). If clust_fun is provided, prob_matrix should be the name of the matrix component as a string (e.g., "u" for fcm).

prob_type

Character string specifying the type transformation of membership matrix considered as proximity matrix in prob_matrix. Options are:

"pp": Posterior probabilities \([\gamma_{ik}]_{n \times K}\) (non-negative, typically summing to 1 per row), treated as similarities
"nlpp": Negative log of posterior probabilities \([-\ln\gamma_{ik}]_{n \times K}\) (non-positive), treated as dissimilarities.
"pd": Probability distribution \([\gamma_{ik}/\pi_{k}]_{n \times K}\) (normalized posterior probabilities relative to cluster proportions \(\pi_{k}\)), treated as similarities.

Defaults to "pp".

method

Character string specifying the silhouette calculation method. Options are "pac" (Probability of Alternative Cluster) or "medoid". Defaults to "pac".

average

Character string specifying the type of average silhouette width calculation. Options are "crisp" (simple average) or "fuzzy" (weighted average based on membership differences). Defaults to "crisp".

a

Numeric value controlling the fuzzifier or weight scaling in fuzzy silhouette averaging. Higher values increase the emphasis on strong membership differences. Must be positive. Defaults to 2.

print.summary

Logical; if TRUE, prints a summary table of average silhouette widths and sizes for each cluster. Defaults to FALSE.

clust_fun

Optional S3 or S4 function object or function as character string specifying a clustering function that produces the proximity measure matrix. For example, fcm or "fcm". If provided, prox_matrix must be the name of the matrix component in the clustering output (e.g., "d" for fcm when proximity_type = "dissimilarity"). Defaults to NULL.

...

Additional arguments passed to clust_fun, such as x,centers for fcm.

Value

A data frame of class "Silhouette" containing cluster assignments, nearest neighbor clusters, silhouette widths for each observation, and weights (for fuzzy clustering). The object includes the following attributes:

proximity_type: The proximity type used ("similarity" or "dissimilarity").
method: The silhouette calculation method used ("medoid" or "pac").

Details

Although the silhouette method was originally developed for evaluating clustering structure within a single result, this implementation allows leveraging cluster membership probabilities from soft clustering methods to construct proximity-based silhouettes. These silhouette widths can be compared heuristically across different algorithms to assess clustering quality.

See doi:10.1080/23737484.2024.2408534 for more details.

References

Raymaekers, J., & Rousseeuw, P. J. (2022). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. Journal of Computational and Graphical Statistics, 31(4), 1332–1343. doi:10.1080/10618600.2022.2050249

Bhat Kapu, S., & Kiruthika. (2024). Some density-based silhouette diagnostics for soft clustering algorithms. Communications in Statistics: Case Studies, Data Analysis and Applications, 10(3-4), 221-238. doi:10.1080/23737484.2024.2408534

Examples

# \donttest{
# Compare two soft clustering algorithms using softSilhouett
# Example: FCM vs. FCM2 on iris data, using average silhouette width as a criterion
data(iris)
if (requireNamespace("ppclust", quietly = TRUE)) {
  fcm_result <- ppclust::fcm(iris[, 1:4], 3)
  out_fcm <- softSilhouette(prob_matrix = fcm_result$u,print.summary = TRUE)
  plot(out_fcm)
  sfcm <- summary(out_fcm, print.summary = FALSE)
} else {
  message("Install 'ppclust' to run this example: install.packages('ppclust')")
}
#> -----------------------------------------
#> Average similarity pac silhouette: 0.7541
#> -----------------------------------------
#> 
#>   cluster size avg.sil.width
#> 1       1   40        0.7005
#> 2       2   50        0.9507
#> 3       3   60        0.6261
#> 
#> Available attributes: names, class, row.names, proximity_type, method
if (requireNamespace("ppclust", quietly = TRUE)) {
  fcm2_result <- ppclust::fcm2(iris[, 1:4], 3)
  out_fcm2 <- softSilhouette(prob_matrix = fcm2_result$u,print.summary = TRUE)
  plot(out_fcm2)
  sfcm2 <- summary(out_fcm2, print.summary = FALSE)
} else {
  message("Install 'ppclust' to run this example: install.packages('ppclust')")
}
#> -----------------------------------------
#> Average similarity pac silhouette: 0.4113
#> -----------------------------------------
#> 
#>   cluster size avg.sil.width
#> 1       1   25        0.3623
#> 2       2   60        0.2666
#> 3       3   65        0.5636
#> 
#> Available attributes: names, class, row.names, proximity_type, method
# Compare average silhouette widths of fcm and fcm2
if (requireNamespace("ppclust", quietly = TRUE)) {
  cat("FCM average silhouette width:", sfcm$avg.width, "\n")
  cat("FCM2 average silhouette width:", sfcm2$avg.width, "\n")
}
#> FCM average silhouette width: 0.7541271 
#> FCM2 average silhouette width: 0.411275 
# }