Evolutionary and Iterative Crisp and Rough Clustering I: Theory

Authors:
Manish Joshi;Pawan Lingras
Affiliations:
Department of Computer Science, North Maharashtra University, Jalgaon, India;Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Canada B3H 3C3
Venue:
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Year:
2009

Citing 9
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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.