Fuzzy clustering with evolutionary algorithms
Frank Klawonn, Annette Keller · International Journal of Intelligent Systems · 1998
Objective function-based fuzzy clustering aims at finding a fuzzy partition by optimizing a function that evaluates a (fuzzy) assignment of a given data set to clusters that are characterized by a set of parameters, the so-called prototypes. The iterative optimization technique usually requires the objective function not only to be differentiable, but prefers also an analytical solution for the equations of necessary conditions for local optima. Evolutionary algorithms are known to be an alternative robust optimization technique which is applicable to quite general forms of objective functions. We investigate the possibility of making use of evolutionary algorithms in fuzzy clustering. Our experiments and theoretical investigations show that the application of evolutionary algorithms to shell clustering, where the clusters are in the form of geometric contours, is not very promising due to the shape of the objective function, whereas they can be helpful in finding solid clusters that are not smooth, for example, rectangles or cubes. These types of clusters play an important role in fuzzy rule extraction from data. © 1998 John Wiley & Sons, Inc.