neokm: Clusters data using the NEOKM (Non-Exhaustive Overlapping...

In COveR: Clustering with Overlaps

Clusters data using the NEOKM (Non-Exhaustive Overlapping K-means) algorithm.

Description

Clusters data using the NEOKM (Non-Exhaustive Overlapping K-means) algorithm.

Usage

neokm(
  x,
  centers,
  alpha = 0.3,
  beta = 0.05,
  nstart = 10,
  trace = FALSE,
  iter.max = 20
)

Arguments

x	A numeric matrix or data frame containing the data to be clustered.
centers	Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it indicates how many clusters to create.
alpha	A numeric value representing the degree of overlap allowed between clusters (default is 0.3).
beta	A numeric value representing non-exhaustiveness, which affects the cluster formation (default is 0.05).
nstart	The number of times to run the NEOKM algorithm with different starting values to find the best result (default is 10).
trace	Logical value indicating whether to show progress of the algorithm (default is 'FALSE').
iter.max	Maximum number of iterations allowed for the NEOKM algorithm (default is 20).

Value

A list of clustering results, including: - 'cluster': Matrix indicating the cluster assignment for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by elements. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': The number of iterations the algorithm executed. - 'overlaps': The average overlap across clusters.

Examples

neokm(iris[, -5], 3)
neokm(iris[, -5], iris[, -5], 1, 2)

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