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Constrained clustering with a complex cluster structure
constrained clustering
model-based clustering
mixture of models
pairwise equivalence constraints
semi-supervised learning
cross-entropy clustering
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.
cris.lastimport.scopus | 2024-04-24T06:13:52Z | |
cris.lastimport.wos | 2024-04-10T00:45:31Z | |
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.affiliation | Wydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej | pl |
dc.contributor.author | Śmieja, Marek - 135996 | pl |
dc.contributor.author | Wiercioch, Magdalena - 208738 | pl |
dc.date.accessioned | 2017-09-12T06:57:04Z | |
dc.date.available | 2017-09-12T06:57:04Z | |
dc.date.issued | 2017 | pl |
dc.date.openaccess | 0 | |
dc.description.accesstime | w momencie opublikowania | |
dc.description.number | 3 | pl |
dc.description.physical | 493-518 | pl |
dc.description.version | ostateczna wersja wydawcy | |
dc.description.volume | 11 | pl |
dc.identifier.doi | 10.1007/s11634-016-0254-x | pl |
dc.identifier.eissn | 1862-5355 | pl |
dc.identifier.issn | 1862-5347 | pl |
dc.identifier.uri | http://ruj.uj.edu.pl/xmlui/handle/item/44074 | |
dc.language | eng | pl |
dc.language.container | eng | pl |
dc.rights | Udzielam licencji. Uznanie autorstwa 3.0 Polska | * |
dc.rights.licence | CC-BY | |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/pl/legalcode | * |
dc.share.type | inne | |
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.subtype | Article | pl |
dc.title | Constrained clustering with a complex cluster structure | pl |
dc.title.journal | Advances in Data Analysis and Classification | pl |
dc.type | JournalArticle | pl |
dspace.entity.type | Publication |