conclust (version 1.1)

ccls: 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

Value

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

Details

This algorithm minimizes the clustering cost function using Tabu search.

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