Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99247
Title: Clustering with multiviewpoint-based similarity measure
Authors: Nguyen, Duc Thang
Chen, Lihui
Chan, Chee Keong
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Nguyen, 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.
Series/Report no.: IEEE transactions on knowledge and data engineering
Abstract: All 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.
URI: https://hdl.handle.net/10356/99247
http://hdl.handle.net/10220/13486
ISSN: 1041-4347
DOI: http://dx.doi.org/10.1109/TKDE.2011.86
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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