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Semi-supervised constrained clustering with cluster outlier filtering

Semi-supervised constrained clustering with cluster outlier filtering
Semi-supervised constrained clustering with cluster outlier filtering
Constrained clustering addresses the problem of creating minimum variance clusters with the added complexity that there is a set of constraints that must be fulfilled by the elements in the cluster. Research in this area has focused on “must-link” and “cannot-link” constraints, in which pairs of elements must be in the same or in different clusters, respectively. In this work we present a heuristic procedure to perform clustering in two classes when the restrictions affect all the elements of the two clusters in such a way that they depend on the elements present in the cluster. This problem is highly susceptible to outliers in each cluster (extreme values that create infeasible solutions), so the procedure eliminates elements with extreme values in both clusters, and achieves adequate performance measures at the same time. The experiments performed on a company database allow to discover a great deal of information, with results that are more readily interpretable when compared to classical k-means clustering
0302-9743
347-354
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
San Martin, Cesar
c4ee8d1f-ee88-47c9-bdc0-216a37d33ba4
Kim, Sang-Woon
0e60a735-7917-4417-98cd-ca03744afe24
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
San Martin, Cesar
c4ee8d1f-ee88-47c9-bdc0-216a37d33ba4
Kim, Sang-Woon
0e60a735-7917-4417-98cd-ca03744afe24

Bravo, Cristian and Weber, Richard , San Martin, Cesar and Kim, Sang-Woon (eds.) (2011) Semi-supervised constrained clustering with cluster outlier filtering. Lecture Notes in Computer Science, 7042, 347-354. (doi:10.1007/978-3-642-25085-9_41).

Record type: Article

Abstract

Constrained clustering addresses the problem of creating minimum variance clusters with the added complexity that there is a set of constraints that must be fulfilled by the elements in the cluster. Research in this area has focused on “must-link” and “cannot-link” constraints, in which pairs of elements must be in the same or in different clusters, respectively. In this work we present a heuristic procedure to perform clustering in two classes when the restrictions affect all the elements of the two clusters in such a way that they depend on the elements present in the cluster. This problem is highly susceptible to outliers in each cluster (extreme values that create infeasible solutions), so the procedure eliminates elements with extreme values in both clusters, and achieves adequate performance measures at the same time. The experiments performed on a company database allow to discover a great deal of information, with results that are more readily interpretable when compared to classical k-means clustering

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More information

Published date: 2011
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 396681
URI: https://eprints.soton.ac.uk/id/eprint/396681
ISSN: 0302-9743
PURE UUID: 69121f0e-66fa-4dd6-b2e8-32217d6ff75a
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

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Date deposited: 10 Jun 2016 10:29
Last modified: 06 Jun 2018 12:35

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Contributors

Author: Cristian Bravo ORCID iD
Author: Richard Weber
Editor: Cesar San Martin
Editor: Sang-Woon Kim

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