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Clustering via Dissimilarity Matrix based on String Distances

Source: R/clusters.R
cluster_data.Rd

Performs clustering using specified dissimilarity measures and clustering methods. The rows of the data are first converted to strings and compared using the dissimilarity measures available in the stringdist package.

Usage

cluster_data(
  data,
  k,
  dissimilarity = "hamming",
  method = "pam",
  na_syms = c("*", "%"),
  weighted = FALSE,
  lambda = 1,
  ...
)

cluster_sequences(
  data,
  k,
  dissimilarity = "hamming",
  method = "pam",
  na_syms = c("*", "%"),
  weighted = FALSE,
  lambda = 1,
  ...
)

Arguments

data

A data.frame or a matrix in wide format.

k

An integer giving the number of clusters.

dissimilarity

A character string specifying the dissimilarity measure. The available options are: "osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", and "jw". See stringdist::stringdist-metrics for more information on these measures.

method

A character string specifying clustering method. The available methods are "pam", "ward.D", "ward.D2", "complete","average", "single", "mcquitty", "median", and "centroid". See cluster::pam() and stats::hclust() for more information on these methods.

na_syms

A character vector of symbols or factor levels to convert to explicit missing values.

weighted

A logical value indicating whether the dissimilarity measure should be weighted (the default is FALSE for no weighting). If TRUE, earlier observations of the sequences receive a greater weight in the distance calculation with an exponential decay. Currently only supported for the Hamming distance.

lambda

A numeric value defining the strength of the decay when weighted = TRUE. The default is 1.0.

...

Additional arguments passed to stringdist::stringdist().

Value

A tna_clustering object which is a list containing:

  • data: The original data.

  • k: The number of clusters.

  • assignments: An integer vector of cluster assignments.

  • silhouette: Silhouette score measuring clustering quality.

  • sizes: An integer vector of cluster sizes.

  • method: The clustering method used.

  • distance: The distance matrix.

Examples

data <- data.frame(
  T1 = c("A", "B", "A", "C", "A", "B"),
  T2 = c("B", "A", "B", "A", "C", "A"),
  T3 = c("C", "C", "A", "B", "B", "C")
)

# PAM clustering with optimal string alignment (default)
result <- cluster_sequences(data, k = 2)