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Constrained clustering with a complex cluster structure

Constrained clustering with a complex cluster structure

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dc.contributor.author Śmieja, Marek [SAP14005333] pl
dc.contributor.author Wiercioch, Magdalena [USOS149779] pl
dc.date.accessioned 2017-09-12T06:57:04Z
dc.date.available 2017-09-12T06:57:04Z
dc.date.issued 2017 pl
dc.identifier.issn 1862-5347 pl
dc.identifier.uri http://ruj.uj.edu.pl/xmlui/handle/item/44074
dc.language eng pl
dc.rights Udzielam licencji. Uznanie autorstwa 3.0 Polska *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/pl/legalcode *
dc.title Constrained clustering with a complex cluster structure pl
dc.type JournalArticle pl
dc.description.physical 493-518 pl
dc.abstract.en 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. pl
dc.subject.en constrained clustering pl
dc.subject.en model-based clustering pl
dc.subject.en mixture of models pl
dc.subject.en pairwise equivalence constraints pl
dc.subject.en semi-supervised learning pl
dc.subject.en cross-entropy clustering pl
dc.description.volume 11 pl
dc.description.number 3 pl
dc.identifier.doi 10.1007/s11634-016-0254-x pl
dc.identifier.eissn 1862-5355 pl
dc.title.journal Advances in Data Analysis and Classification pl
dc.language.container eng pl
dc.affiliation Wydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej pl
dc.subtype Article pl
dc.rights.original CC-BY; inne; ostateczna wersja wydawcy; w momencie opublikowania; 0; pl
.pointsMNiSW [2017 A]: 35


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Udzielam licencji. Uznanie autorstwa 3.0 Polska Except where otherwise noted, this item's license is described as Udzielam licencji. Uznanie autorstwa 3.0 Polska