okm: Clusters data using the OKM (Overlapping K-Means) clustering...

In COveR: Clustering with Overlaps

Clusters data using the OKM (Overlapping K-Means) clustering algorithm.

Description

Clusters data using the OKM (Overlapping K-Means) clustering algorithm.

Usage

okm(x, centers, iter.max = 10, nstart = 1, trace = FALSE, method = "euclid")

Arguments

x	A numeric data matrix or data frame containing the data to be clustered.
centers	Either a positive integer indicating the number of clusters to create or a matrix of pre-initialized cluster centers.
iter.max	Maximum number of iterations allowed for the clustering algorithm (default is 10).
nstart	Number of random initializations to find the best result (default is 1).
trace	Logical value indicating whether to display the progress of the algorithm (default is 'FALSE').
method	A string specifying the distance metric to use; options are 'euclid' (Euclidean distance) or 'manhattan' (Manhattan distance) (default is "euclid").

Value

A list containing the clustering results, including: - 'cluster': Matrix indicating the cluster assignments for each data point. - 'centers': The final cluster centers. - 'tot.withinss': Total within-cluster sum of squares. - 'overlaps': The measure of overlap among clusters.

Examples

okm(iris[, -5], 3)

COveR documentation built on Oct. 30, 2024, 9:28 a.m.