Clustering with multiviewpoint-based similarity measure
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...
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sg-ntu-dr.10356-992472020-03-07T13:57:28Z Clustering with multiviewpoint-based similarity measure Nguyen, Duc Thang Chen, Lihui Chan, Chee Keong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 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. 2013-09-16T07:14:36Z 2019-12-06T20:05:02Z 2013-09-16T07:14:36Z 2019-12-06T20:05:02Z 2012 2012 Journal Article 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. 1041-4347 https://hdl.handle.net/10356/99247 http://hdl.handle.net/10220/13486 10.1109/TKDE.2011.86 en IEEE transactions on knowledge and data engineering © 2012 IEEE |
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DRNTU::Engineering::Electrical and electronic engineering Nguyen, Duc Thang Chen, Lihui Chan, Chee Keong Clustering with multiviewpoint-based similarity measure |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Nguyen, Duc Thang Chen, Lihui Chan, Chee Keong |
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Article |
author |
Nguyen, Duc Thang Chen, Lihui Chan, Chee Keong |
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Nguyen, Duc Thang |
title |
Clustering with multiviewpoint-based similarity measure |
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Clustering with multiviewpoint-based similarity measure |
title_full |
Clustering with multiviewpoint-based similarity measure |
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Clustering with multiviewpoint-based similarity measure |
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Clustering with multiviewpoint-based similarity measure |
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clustering with multiviewpoint-based similarity measure |
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2013 |
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https://hdl.handle.net/10356/99247 http://hdl.handle.net/10220/13486 |
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