conclust-package {conclust}

Pairwise Constraints Clustering

Description

There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.

Details

The DESCRIPTION file:

Package:	conclust
Type:	Package
Title:	Pairwise Constraints Clustering
Version:	1.1
Date:	2016-08-15
Author:	Tran Khanh Hiep, Nguyen Minh Duc
Maintainer:	Tran Khanh Hiep <[e-mail]>
Description:	There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
License:	GPL-3

Index of help topics:

ccls                    Pairwise Constrained Clustering by Local Search
ckmeans                 COP K-means algorithm
conclust-package        Pairwise Constraints Clustering
lcvqe                   LCVQE algorithm
mpckm                   MPC K-means algorithm

There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.

Author(s)

Tran Khanh Hiep, Nguyen Minh Duc

Maintainer: Tran Khanh Hiep <[e-mail]>

References

Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets

See Also

Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets

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