ikmeans: 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
)

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.
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).

Examples

Run this code

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

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