ineokm: 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
)

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.

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).

Examples

Run this code

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

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