Clustering using Multi-objective Genetic Algorithm and its Application to Image Segmentation
Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay, Ujjwal Maulik · 2006
This article presents a multiobjective fuzzy genetic clustering technique employing real coded encoding of cluster centers. 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. This fact has motivated us to develop a multiobjective fuzzy genetic clustering method that optimizes multiple validity measures simultaneously. User can chose any partitioning result from the resultant set of non dominated solutions according to the problem requirements. A number of artificial and real-life data sets have been clustered using the proposed fuzzy clustering method. Also the proposed algorithm has been applied for segmentation of a remote sensing image to show its effectiveness in pixel classification.