r2okm: Cluster data using the R2-OKM algorithm.

In COveR: Clustering with Overlaps

Cluster data using the R2-OKM algorithm.

Description

Cluster data using the R2-OKM algorithm.

Usage

r2okm(x, centers, lambda = 0, nstart = 10, trace = FALSE, iter.max = 20)

Arguments

x	A numeric data matrix or data frame containing the data to be clustered.
centers	Either a positive integer specifying the number of clusters to create or a matrix of initial cluster centers.
lambda	A numeric parameter that controls the clustering behavior, influencing the shape and separation of clusters (default is 0).
nstart	Number of random initializations to find the best clustering result (default is 10).
trace	Logical value indicating whether to display progress information during execution (default is 'FALSE').
iter.max	Maximum number of iterations allowed for the clustering algorithm (default is 20).

Value

A list containing the clustering results, which includes: - 'cluster': Matrix indicating the cluster assignments for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares for each cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': Number of data points in each cluster. - 'iter': Number of iterations performed. - 'overlaps': Average number of clusters that each point overlaps with.

Examples

r2okm(iris[, -5], 3)
r2okm(iris[, -5], 3, lambda = 0.3)
r2okm(iris[, -5], iris[, -5], lambda = 1)

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