Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering
Pawan J. Lingras, Manish Joshi · International Journal of Computational Intelligence Systems · 2011
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.