A cluster estimation method with extension to fuzzy model identification

Abstract

We present an efficient method for estimating cluster centers of numerical data. This method can be used to determine the number of clusters and their initial values for initializing iterative optimization-based clustering algorithms such as fuzzy C-means. Here were combine this cluster estimation method with a least squares estimation algorithm to provide a fast and robust method for identifying fuzzy models from input/output data. A benchmark problem involving the prediction of a chaotic time series shows this method compares favourably with other more compositionally intensive methods.<>