ikmeans: Performs k-means clustering on interval data, allowing for...

In COveR: Clustering with Overlaps

Performs k-means clustering on interval data, allowing for partitioning of data points into distinct clusters.

Description

Performs k-means clustering on interval data, allowing for partitioning of data points into distinct clusters.

Usage

ikmeans(
  x,
  centers,
  nstart = 10,
  distance = "euclid",
  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.
nstart	The number of times to run the k-means algorithm with different starting values in order to find the best solution (default is 10).
distance	A string specifying the distance metric to use: 'euclid' for Euclidean distance or 'hausdorff' for Hausdorff distance (default is 'euclid').
trace	Logical value indicating whether to show progress of the algorithm (default is 'FALSE').
iter.max	Maximum number of iterations allowed for the k-means 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

ikmeans(iaggregate(iris, col = 5), 2)
ikmeans(iaggregate(iris, col = 5), iaggregate(iris, col = 5))

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