KMeansClustering: (Sparse) K-means clustering

In sharp: Stability-enHanced Approaches using Resampling Procedures

(Sparse) K-means clustering

Description

Runs k-means clustering using implementation from kmeans. This function is not using stability.

Usage

KMeansClustering(xdata, nc = NULL, Lambda = NULL, ...)

Arguments

xdata	data matrix with observations as rows and variables as columns.
nc	matrix of parameters controlling the number of clusters in the underlying algorithm specified in implementation. If nc is not provided, it is set to seq(1, tau*nrow(xdata)).
Lambda	vector of penalty parameters (see argument wbounds in KMeansSparseCluster).
...	additional parameters passed to kmeans (if Lambda is NULL) or KMeansSparseCluster.

Value

A list with:

comembership	an array of binary and symmetric co-membership matrices.
weights	a matrix of median weights by feature.

References

\insertRef

SparseClusteringsharp

See Also

Other clustering algorithms: DBSCANClustering(), GMMClustering(), HierarchicalClustering(), PAMClustering()

Examples

# Data simulation
set.seed(1)
simul <- SimulateClustering(n = c(10, 10), pk = 50)

# K means clustering
mykmeans <- KMeansClustering(xdata = simul$data, nc = seq_len(20))

# Sparse K means clustering
if (requireNamespace("sparcl", quietly = TRUE)) {
  mykmeans <- KMeansClustering(
    xdata = simul$data, nc = seq_len(20),
    Lambda = c(2, 5)
  )
}

sharp documentation built on April 11, 2025, 5:44 p.m.