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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bock, H.H., Diday, E. (eds.): Analysis of symbolic data. Springer, Heidelberg (2000)
Denoeux, T., Masson, M.: Multidimensional scaling of interval valued dissimilarity data. Pattern Recognition Letters 21, 83–92 (2000)
Gowda, K.C., Krishna, G.: Agglomerative clustering using the concept of Mutual Nearest Neighborhood. Pattern Recognition 10, 105–112 (1977)
Gowda, K.C., Diday, E.: Symbolic Clustering using a new dissimilarity measure. Pattern Recognition 24(6), 567–578 (1991)
Gowda, K.C., Diday, E.: Symbolic clustering using a new similarity measure. IEEE Trans SMC 22(2), 368–378 (1992)
Gowda, K.C., Ravi, T.V.: Agglomerative clustering of Symbolic Objects using the concepts of both similarity and dissimilarity. Pattern Recognition Letters 16, 647–652 (1995a)
Gowda, K.C., Ravi, T.V.: Divisive Clustering of Symbolic Objects using the concepts of both similarity and dissimilarity. Pattern Recognition 28(8), 1277–1282 (1995b)
Guru, D.S., Kiranagi, B.B., Nagabhushan, P.: Multivalued type proximity measure and concept of mutual dissimilarity value for clustering symbolic patterns. Pattern Recognition 38(1), 151–156 (2004)
Guru, D.S., Kiranagi, B.B.: Multivalued type dissimilarity measure and the concept of mutual similarity value useful for clustering symbolic patterns. Pattern Recognition Letters 25(10), 1203–1213 (2005)
Ichino, M., Yaguchi, H.: Generalized Minkowski metrices for mixed feature type data analysis. IEEE Trans on System, Man and Cybernetics 24(4) (April 1994)
Jain, A.K., Dubes, C.R.: Algorithms for Clustering Data. Prentice Hall, Engle Wood Cliffs (1988)
Prakash, S.H.N.: Classification of remotely sensed data: some new approaches. Ph.D. Thesis. University of Mysore. Mysore, India (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guru, D.S., Nagendraswamy, H.S. (2006). Clustering of Interval-Valued Symbolic Patterns Based on Mutual Similarity Value and the Concept of k-Mutual Nearest Neighborhood. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_24
Download citation
DOI: https://doi.org/10.1007/11612704_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
eBook Packages: Computer ScienceComputer Science (R0)