Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression

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.