A new multi-objective technique for differential fuzzy clustering

Abstract

This article presents a new multiobjective differential evolution based fuzzy clustering technique. Recent research has shown that clustering techniques that optimize a single objective may not provide satisfactory result because no single validity measure works well on different kinds of data sets. The fact motivated us to present a new multiobjective Differential Evolution based fuzzy clustering technique that encodes the cluster centres in its vectors and optimizes multiple validity measures simultaneously. In the final generation, it produces a set of non-dominated solutions, from which the user can relatively judge and pick up the most promising one according to the problem requirements. Superiority of the proposed method over its single objective versions, multiobjective version of classical differential evolution and genetic algorithm, well-known fuzzy C-means and average linkage clustering algorithms has been demonstrated quantitatively and visually for several synthetic and real life data sets. Statistical significance test has been conducted to establish the statistical superiority of the proposed multiobjective clustering approach. Finally, the proposed algorithm has been applied for segmentation of a remote sensing image to show its effectiveness in pixel classification.