Classification of symbolic data using fuzzy set theory

Author

Dinesh, M.S. ; Gowda, K. Chidananda ; Ravi, T.V.

Author_Institution

Dept. of Comput. Sci. & Eng., Mysore Univ., India

Volume

2

fYear

1997

fDate

27-23 May 1997

Firstpage

383

Abstract

Proposes a new algorithm to carry out classification of symbolic data using fuzzy set theory without any a priori assumption. The aim is to show how to apply fuzzy concepts to symbolic data. The new algorithm involves two stages. In the first stage, the number of classes present in the data is found using a cluster indicator, and in the second stage, fuzzy descriptions on symbolic data have been developed. The proposed work is new in the sense that no research work has previously been reported on the application of fuzzy concepts to symbolic data classification. The results of the proposed algorithm are compared with other symbolic clustering techniques

Keywords

data analysis; fuzzy set theory; pattern classification; symbol manipulation; class number; cluster indicator; fuzzy descriptions; fuzzy set theory; symbolic clustering techniques; symbolic data classification algorithm; Clustering algorithms; Computer science; Data analysis; Data structures; Decision making; Educational institutions; Fuzzy set theory; Fuzzy sets; Merging; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Electronic Systems, 1997. KES '97. Proceedings., 1997 First International Conference on

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-3755-7

Type

conf

DOI

10.1109/KES.1997.619413

Filename

619413