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dc.contributor.authorLiu, Chien-Liangen_US
dc.contributor.authorHsaio, Wen-Hoaren_US
dc.contributor.authorChang, Tao-Hsingen_US
dc.contributor.authorJou, Tzai-Minen_US
dc.date.accessioned2018-08-21T05:52:45Z-
dc.date.available2018-08-21T05:52:45Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn1088-467Xen_US
dc.identifier.urihttp://dx.doi.org/10.3233/IDA-160105en_US
dc.identifier.urihttp://hdl.handle.net/11536/143923-
dc.description.abstractMulti-label learning has attracted significant attention from machine learning and data mining over the last decade. Although many multi-label classification algorithms have been devised, few research studies focus on multi-assignment clustering (MAC), in which a data instance can be assigned to multiple clusters. The MAC problem is practical in many application domains, such as document clustering, customer segmentation and image clustering. Additionally, specifying the number of clusters is always a difficult but critical problem for a certain class of clustering algorithms. Hence, this work proposes a non-parametric multi-assignment clustering algorithm called multi-assignment Chinese restaurant process (MACRP), which allows the model complexity to grow as more data instances are observed. The proposed algorithm determines the number of clusters from data, so it provides a practical model to process massive data sets. In the proposed algorithm, we devise a novel prior distribution based on the similarity graph to achieve the goal of multi-assignment, and propose a Gibbs sampling algorithm to carry out posterior inference. The implementation in this work uses collapsed Gibbs sampling and compares with several methods. Additionally, previous evaluation metrics used by multi-label classification are inappropriate for MAC, since label information is unavailable. This work further devises an evaluation metric for MAC based on the characteristics of clustering and multi-assignment problems. We conduct experiments on two real data sets, and the experimental results indicate that the proposed method is competitive and outperforms the alternatives on most data sets.en_US
dc.language.isoen_USen_US
dc.subjectMulti-assignment clusteringen_US
dc.subjectChinese restaurant process (CRP)en_US
dc.subjectNon-parametric Bayesianen_US
dc.titleNonparametric multi-assignment clusteringen_US
dc.typeArticleen_US
dc.identifier.doi10.3233/IDA-160105en_US
dc.identifier.journalINTELLIGENT DATA ANALYSISen_US
dc.citation.volume21en_US
dc.citation.spage893en_US
dc.citation.epage911en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000412919200008en_US
Appears in Collections:Articles