Entropy-based fuzzy clustering and fuzzy modeling

Fuzzy clustering is capable of finding vague boundaries that crisp clusteri ng fails to obtain. But time complexity of fuzzy clustering is usually high , and the need to specify complicated parameters hinders its use. In this p aper, an entropy-based fuzzy clustering method is proposed. It automaticall y identifies the number and initial locations of cluster centers. It calcul ates the entropy at each data point and selects the data point with minimum entropy as the first cluster center. Next it removes all data points havin g similarity larger than a threshold with the chosen duster center. This pr ocess is repeated till all data points are removed. Unlike previous methods of its kind, it does not need to revise entropy value for each data point after a cluster center is determined. This saves a lot of time. Also it req uires just two parameters that are easy to specify. It is able to find the natural clusters in the data. The clustering method is also extended to con struct a rule-based fuzzy model. A new way of estimating initial membership functions for fuzzy sets is presented. The experimental results show that the fuzzy model is good in predicting output variable values. (C) 2000 Else vier Science B.V. All rights reserved.