Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering

DOI

10.2991/ijcis.2011.4.1.2How to use a DOI?

Keywords

Keywords: Rough Clustering, Crisp Clustering, GA based Clustering, Cluster Quality.

Abstract

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.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Pawan Lingras
AU  - Manish Joshi
PY  - 2011
DA  - 2011/02/01
TI  - Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering
JO  - International Journal of Computational Intelligence Systems
SP  - 12
EP  - 28
VL  - 4
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.1.2
DO  - 10.2991/ijcis.2011.4.1.2
ID  - Lingras2011
ER  -