conclust-package: Pairwise Constraints Clustering

In 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

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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

Example output

[1] 2 2 1 1
[1] 1 1 2 2
[1] 2 2 1 1
[1] 1 1 2 2

conclust documentation built on May 2, 2019, 1:07 p.m.