Volume 4, Issue 1, February 2011, Pages 12 - 28
Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering
Pawan Lingras, Manish Joshi
Received 19 November 2009, Accepted 20 September 2010, Available Online 1 February 2011.
- https://doi.org/10.2991/ijcis.2011.4.1.2How to use a DOI?
- Keywords: Rough Clustering, Crisp Clustering, GA based Clustering, Cluster Quality.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Pawan Lingras AU - Manish Joshi PY - 2011 DA - 2011/02 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 - https://doi.org/10.2991/ijcis.2011.4.1.2 ID - Lingras2011 ER -