Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97853
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dc.contributor.authorYan, Yangen
dc.contributor.authorChen, Lihuien
dc.contributor.authorNguyen, Duc Thangen
dc.date.accessioned2013-07-26T06:49:04Zen
dc.date.accessioned2019-12-06T19:47:22Z-
dc.date.available2013-07-26T06:49:04Zen
dc.date.available2019-12-06T19:47:22Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationYan, Y., Chen, L., & Nguyen, D. T. (2012). Semi-supervised clustering with multi-viewpoint based similarity measure. The 2012 International Joint Conference on Neural Networks (IJCNN).en
dc.identifier.urihttps://hdl.handle.net/10356/97853-
dc.description.abstractThe traditional (dis)similarity measure between a pair of data objects in a clustering method uses only a single viewpoint, which is usually the origin as the only reference point. Recently a novel multi-viewpoint based similarity (MVS) measure [1] has been proposed, which utilizes many different viewpoints in similarity measure and it has been successfully applied in data clustering. In this paper, we study how a semi-supervised MVS-based clustering can be developed by incorporating some prior knowledge in the form of class labels, when they are available to the user. A novel search-based semi-supervised clustering method called CMVS is proposed in the MVS manner with the help of a small percentage of objects being labeled. Two new criterion functions for clustering have been formulated accordingly, when only these labeled objects are considered as the viewpoints in the multi-viewpoints based similarity measure. Theoretical discussion has been conducted to ensure the newly proposed criterion functions make good use of the prior knowledge in terms of similarity measure, besides seeding. Empirical study is performed on various benchmark datasets to demonstrate the effectiveness and verify the merit of our proposed semi-supervised MVS clustering.en
dc.language.isoenen
dc.rights© 2012 IEEE.en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleSemi-supervised clustering with multi-viewpoint based similarity measureen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceInternational Joint Conference on Neural Networks (2012 : Brisbane, Australia)en
dc.identifier.doi10.1109/IJCNN.2012.6252650en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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