mpckm {conclust}

MPC K-means algorithm

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

mpckm(data, k, mustLink, cantLink, maxIter = 10)

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

Details

This algorithm finds a clustering that satisfies as many constraints as possible

Value

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

Note

This is one of the best algorithm for clustering with constraints.

Author(s)

Tran Khanh Hiep Nguyen Minh Duc

References

Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering

See Also

Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering

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 = mpckm(data, k, mustLink, cantLink)
pred