fuzzy_icmeans: Performs fuzzy c-means clustering on interval data, allowing for soft clustering of data points into multiple clusters.

Description

Performs fuzzy c-means clustering on interval data, allowing for soft clustering of data points into multiple clusters.

Usage

fuzzy_icmeans(
  x,
  centers,
  m = 2,
  nstart = 2,
  distance = "euclid",
  trace = FALSE,
  iter.max = 40
)

Value

A list of clustering results, including: - `cluster`: The membership matrix indicating the degree of belonging of each data point to each cluster. - `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`: Sizes of each cluster. - `iter`: Number of iterations run by the algorithm. - `overlaps`: The average overlap among clusters.

Arguments

x: A 3D interval array representing the data to be clustered.
centers: Either the number of clusters or a set of pre-initialized cluster centers. If a number is provided, it specifies how many clusters to create.
m: A number greater than 1 that controls the degree of fuzziness in the clustering process (default is 2).
nstart: Number of times to run the clustering algorithm with different starting values to find the best solution (default is 2).
distance: A string specifying the distance metric to use, either 'euclid' for Euclidean distance or 'hausdorff' for Hausdorff distance (default is 'euclid').
trace: Logical, if `TRUE`, tracing information on the progress of the algorithm is displayed (default is `FALSE`).
iter.max: Maximum number of iterations allowed for the clustering algorithm (default is 40).

Examples

Run this code

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

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