CLUSTERING WITH MULTIVIEWPOINT BASED SIMILARITY MEASURE PDF

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 multi-viewpoint based similarity measure and two related clustering methods. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim.

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Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. Clustering with Multiviewpoint-Based Similarity Measure 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.

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.

Article :. Date of Publication: 05 April DOI: Need Help?

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CLUSTERING WITH MULTIVIEWPOINT BASED SIMILARITY MEASURE PDF

The aim of this work is to produce fast, easy-to-apply but effective algorithms for clustering large text collections. In this paper, we propose a novel concept of similarity measure among objects and its related clustering algorithms. The similarity between two objects within a cluster is measured from the view of all other objects outside that cluster. As a result, two optimality criteria are formulated as the objective functions for the clustering problem.

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Clustering with Multi-Viewpoint based Similarity Measure

Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. 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.

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