conclust-package {conclust}R Documentation

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 <hieptkse03059@fpt.edu.vn>
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 <hieptkse03059@fpt.edu.vn>

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

[Package conclust version 1.1 Index]