conclust-package {conclust} | R Documentation |
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.
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.
Tran Khanh Hiep, Nguyen Minh Duc
Maintainer: Tran Khanh Hiep <hieptkse03059@fpt.edu.vn>
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
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
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