DocumentCode :
1282651
Title :
Hierarchical unsupervised fuzzy clustering
Author :
Geva, Amir B.
Author_Institution :
Dept. of Electr. Eng. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
Volume :
7
Issue :
6
fYear :
1999
fDate :
12/1/1999 12:00:00 AM
Firstpage :
723
Lastpage :
733
Abstract :
A recursive algorithm for hierarchical fuzzy partitioning is presented. The algorithm has the advantages of hierarchical clustering, while maintaining fuzzy clustering rules. Each pattern can have a nonzero membership in more than one subset of the data in the hierarchy. Optimal feature extraction and reduction is optionally reapplied for each subset. Combining hierarchical and fuzzy concepts is suggested as a natural feasible solution to the cluster validity problem of real data. The convergence and membership conservation of the algorithm are proven. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class
Keywords :
convergence; covariance matrices; feature extraction; fuzzy set theory; pattern clustering; cluster validity problem; hierarchical fuzzy partitioning; hierarchical unsupervised fuzzy clustering; membership conservation; optimal feature extraction; recursive algorithm; Clustering algorithms; Clustering methods; Covariance matrix; Dynamic range; Feature extraction; Fuzzy sets; Magnetic resonance imaging; Partitioning algorithms; Pattern recognition; Positron emission tomography;
fLanguage :
English
Journal_Title :
Fuzzy Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1063-6706
Type :
jour
DOI :
10.1109/91.811242
Filename :
811242
Link To Document :
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