Evolutionary and Iterative Crisp and Rough Clustering I: Theory

Abstract

Researchers have proposed several Genetic Algorithms (GA) based clustering algorithms for crisp and rough clustering. In this two part series of papers, we compare the effect of GA optimization on resulting cluster quality of K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms. In this first part, we present the theoretical foundation of the transformation of the crisp clustering K-means and K-medoid algorithms into rough and evolutionary clustering algorithms. The second part of the paper will present experiments with a real world data set, and a standard data set.