Constrained clustering with a complex cluster structure

Marek Śmieja () and Magdalena Wiercioch ()
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Marek Śmieja: Jagiellonian University
Magdalena Wiercioch: Jagiellonian University

Advances in Data Analysis and Classification, 2017, vol. 11, issue 3, No 4, 493-518

Abstract: Abstract In this contribution we present a novel constrained clustering method, Constrained clustering with a complex cluster structure (C4s), which incorporates equivalence constraints, both positive and negative, as the background information. C4s is capable of discovering groups of arbitrary structure, e.g. with multi-modal distribution, since at the initial stage the equivalence classes of elements generated by the positive constraints are split into smaller parts. This provides a detailed description of elements, which are in positive equivalence relation. In order to enable an automatic detection of the number of groups, the cross-entropy clustering is applied for each partitioning process. Experiments show that the proposed method achieves significantly better results than previous constrained clustering approaches. The advantage of our algorithm increases when we are focusing on finding partitions with complex structure of clusters.

Keywords: Constrained clustering; Model-based clustering; Mixture of models; Pairwise equivalence constraints; Semi-supervised learning; Cross-entropy clustering; Primary 68T Computer Science; Secondary 62H30 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11634-016-0254-x

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