Clustering of interval-valued symbolic patterns based on mutual similarity value and the concept of k-mutual nearest neighborhood

Authors:
D. S. Guru;H. S. Nagendraswamy
Affiliations:
Department of Studies in Computer Science, University of Mysore, Mysore, India;Department of Studies in Computer Science, University of Mysore, Mysore, India
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Year:
2006

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Abstract

In this paper, a novel similarity measure for estimating the degree of similarity between two symbolic patterns, the features of which are of interval type is proposed. A method for clustering data patterns based on the mutual similarity value (MSV) and the concept of k-mutual nearest neighbourhood is explored. The concept of mutual nearest neighbourhood exploits the mutual closeness possessed by the patterns for clustering thereby providing the naturalistic proximity characteristics of the patterns. Experiments on various datasets have been conducted in order to study the efficacy of the proposed methodology.