Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression

Author

Hoppner, F. ; Klawonn, Frank

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Univ. of Appl. Sci., Emden, Germany

Volume

1

fYear

2000

fDate

2000

Firstpage

162

Abstract

In this paper, we develop an objective function-based clustering algorithm to build fuzzy models of the Takagi-Sugeno (TS) type automatically from data. In contrast to most of the TS models that can be found in the literature, we decided to use very simple input-space partitions and a higher degree of consequence polynomials (quadratic). Only in this way can transparency and interpretability be guaranteed. We also show how to derive linguistic labels for the polynomials found by the algorithm

Keywords

computational linguistics; fuzzy set theory; pattern clustering; polynomials; statistical analysis; Takagi-Sugeno fuzzy models; fuzzy c-means; fuzzy clustering; fuzzy regression; input-space partitions; interpretability; interpretable fuzzy models; linguistic approximation; linguistic labels; objective function-based clustering algorithm; quadratic consequence polynomials; transparency; Clustering algorithms; Electronic mail; Function approximation; Fuzzy sets; Fuzzy systems; Hybrid intelligent systems; Partitioning algorithms; Piecewise linear approximation; Polynomials; Takagi-Sugeno model;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on

Conference_Location

Brighton

Print_ISBN

0-7803-6400-7

Type

conf

DOI

10.1109/KES.2000.885783

Filename

885783