Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99247
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dc.contributor.authorNguyen, Duc Thangen
dc.contributor.authorChen, Lihuien
dc.contributor.authorChan, Chee Keongen
dc.date.accessioned2013-09-16T07:14:36Zen
dc.date.accessioned2019-12-06T20:05:02Z-
dc.date.available2013-09-16T07:14:36Zen
dc.date.available2019-12-06T20:05:02Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationNguyen, D. T., Chen, L., & Chan, C. K. (2012). Clustering with Multiviewpoint-Based Similarity Measure. IEEE Transactions on Knowledge and Data Engineering, 24(6), 988-1001.en
dc.identifier.issn1041-4347en
dc.identifier.urihttps://hdl.handle.net/10356/99247-
dc.identifier.urihttp://hdl.handle.net/10220/13486en
dc.description.abstractAll clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multiviewpoint-based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on knowledge and data engineeringen
dc.rights© 2012 IEEEen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleClustering with multiviewpoint-based similarity measureen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doihttp://dx.doi.org/10.1109/TKDE.2011.86en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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