ccls {conclust}

R Documentation

Pairwise Constrained Clustering by Local Search

Description

This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.

Usage

ccls(data, k = -1, mustLink, cantLink, maxIter = 1, tabuIter = 100, tabuLength = 20)

Arguments

data	The unlabeled dataset.
k	Number of clusters.
mustLink	A list of must-link constraints
cantLink	A list of cannot-link constraints
maxIter	Number of iteration
tabuIter	Number of iteration in Tabu search
tabuLength	The number of elements in the Tabu list

Details

This algorithm minimizes the clustering cost function using Tabu search.

Value

A vector that represents the labels (clusters) of the data points

Note

This is the first algorithm for pairwise constrained clustering by local search.

Author(s)

Tran Khanh Hiep Nguyen Minh Duc

References

Tran Khanh Hiep, Nguyen Minh Duc, Bui Quoc Trung (2016), Pairwise Constrained Clustering by Local Search.

See Also

Tran Khanh Hiep, Nguyen Minh Duc, Bui Quoc Trung (2016), Pairwise Constrained Clustering by Local Search.

Examples

data = matrix(c(0, 1, 1, 0, 0, 0, 1, 1), nrow = 4)
mustLink = matrix(c(1, 2), nrow = 1)
cantLink = matrix(c(1, 4), nrow = 1)
k = 2
pred = ckmeans(data, k, mustLink, cantLink)
pred

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