ineokm: Performs clustering on interval data using the Neo-KM...

In COveR: Clustering with Overlaps

Performs clustering on interval data using the Neo-KM algorithm, which allows for overlapping and non-exhaustive cluster membership.

Description

Performs clustering on interval data using the Neo-KM algorithm, which allows for overlapping and non-exhaustive cluster membership.

Usage

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

Arguments

x	A 3D interval array representing 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 specifies how many clusters to create.
alpha	A numeric value that controls the degree of overlap between clusters (default is 0.3).
beta	A numeric value that controls the non-exhaustiveness of clusters (default is 0.05).
nstart	The number of times to run the Neo-KM algorithm with different starting values in order to find the best solution (default is 10).
trace	Logical value indicating whether to show the progress of the algorithm (default is 'FALSE').
iter.max	Maximum number of iterations allowed for the Neo-KM algorithm (default is 20).

Value

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

Examples

ineokm(iaggregate(iris, col = 5), 3)
ineokm(iaggregate(iris, col = 5), iaggregate(iris, col = 5), 1, 2)

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