Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering

Researchers have proposed several Genetic Algorithm (GA) based crisp clustering algorithms. Rough clustering based on Genetic Algorithms, Kohonen Self-Organizing Maps, K-means algorithm are also reported in literature. Recently, researchers have combined GAs with iterative rough clustering algorithms such as K-means and K-Medoids. Use of GAs makes it possible to specify explicit optimization of cluster validity measures. However, it can result in additional computing time. In this paper we compare results obtained using K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms. We experimented with a synthetic data set, a real world data set, and a standard dataset using a total within cluster variation, average precision, and execution time required as the criteria for comparison.