Fuzzy clustering for symbolic data

Author

El-Sonbaty, Yasser ; Ismail, M.A.

Author_Institution

Dept. of Electr. & Comput. Eng., Arab Academy for Science & Technology, Alexandria, Egypt

Volume

6

Issue

2

fYear

1998

fDate

5/1/1998 12:00:00 AM

Firstpage

195

Lastpage

204

Abstract

Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which utilizes the concept of agglomerative or divisive methods as the core of the algorithm. The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this concept in formulating the clustering problem of symbolic objects as a partitioning problem. Finally, a fuzzy symbolic c-means algorithm is introduced as an application of applying and testing the proposed algorithm on real and synthetic data sets. The results of the application of the new algorithm show that the new technique is quite efficient and, in many respects, superior to traditional methods of hierarchical nature

Keywords

fuzzy set theory; minimisation; pattern recognition; fuzziness; fuzzy clustering; partitioning problem; symbolic data; symbolic objects; Area measurement; Clustering algorithms; Computer science; Data analysis; Data structures; Fuzzy sets; Helium; Partitioning algorithms; Position measurement; Testing;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.669013

Filename

669013