Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression

Obtaining Interpretable Fuzzy Models from Fuzzy Clustering and Fuzzy Regression* Frank Hiippner Frank Klawonn University of Applied Sciences, Emden Department of Electrical Engineering and Computer Science Constantiaplatz 4 D-26723 Emden, Germany e-mail alias: [email protected] In this paper we develop an objective finctionbased clustering algorithm to build fizzy models of the Takagi-Sugeno (TS) type automatically from data. In contraPt to most of the TS models that cun be found in the literature, we decided to we very simple input-space partitions and a higher degree of consequence polynomials (quadratic). Only in this way transparency and intevretability cun be guaranteed. We also show how to derive linguistic labels for the polynomials found by the algorithm.