conclust (version 1.1)

conclust-package: \Sexpr[results=rd,stage=build]{tools:::Rd_package_title("#1")}conclustPairwise Constraints Clustering

Description

\Sexpr[results=rd,stage=build]{tools:::Rd_package_description("#1")}conclustThere 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: \Sexpr[results=rd,stage=build]{tools:::Rd_package_DESCRIPTION("#1")}conclustThis package was not yet installed at build time.

\Sexpr[results=rd,stage=build]{tools:::Rd_package_indices("#1")}conclust Index: This package was not yet installed at build time.

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.

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