Paper Info
Title
Dynamic Fuzzy Clustering Using Fuzzy Cluster Loading
Abstract
When we obtain clusters through the classification of a given data it is important to interpret the meaning of the obtained clusters. This is particularly true in the clustering of 3-way asymmetric similarity data. This is true because the asymmetric property and the structure of similarity in each cluster are changed over the time periods (or situations), and the interpretation of the obtained clusters is also changed according to the time periods (or situations) or according to the direction of asymmetry.Therefore, this paper proposes a model for capturing the dynamic interpretation of the obtained clusters according to the time periods (or situations) or the direction of asymmetry of 3-way asymmetric similarity data using the idea of the dynamic fuzzy clustering model (M. Sato-Ilic, Y. Sato, "A dynamic additive fuzzy clustering model", in Advances in Data Science and Classification, A. Rizzi et at., Eds, Berlin: Springer-Verlag, 1998) and fuzzy cluster loading (M. Sato-Ilic, "On kernel based fuzzy cluster loadings with the interpretation of the fuzzy clustering result", Int. J. Comput. Numer Anal. Appl., 4(3), pp. 265-278.2003).
Year
DOI
Venue
2006
10.1080/03081070500422620
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS
Keywords
DocType
Volume
asymmetric similarity data, cluster loading, dynamic change, 3-way data, asymmetric aggregation operator, super-matrix
Journal
35
Issue
ISSN
Citations 
2
0308-1079
1
PageRank 
References 
Authors
0.35
3
1
Name
Order
Citations
PageRank
Mika Sato-Ilic13216.09