Evolutionary and Iterative Crisp and Rough Clustering II: Experiments

  • Manish Joshi
  • Pawan Lingras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


In this second part of the paper, we compare the cluster quality of K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms. We experimented with a real world data set, and a standard data set using total within cluster variation, precision and execution time as the measures of comparison.


Rough Clustering Crisp Clustering GA based Clustering Cluster Quality 


  1. 1.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, Irvine, CA (2007), Google Scholar
  2. 2.
    Latiff, N.M.A., Tsimenidis, C.C., Sharif, B.S.: Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks (2007)Google Scholar
  3. 3.
    Lingras, P.: Applications of Rough Set Based K-Means, Kohonen SOM, GA Clustering. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W.P. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 120–139. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Lingras, P., Chen, M., Miao, D.: Rough Multi-category Decision Theoretic Framework. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 676–683. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Małyszkoa, D., Wierzchoń, Sławomir, T.: Standard and Genetic k-means Clustering Techniques in Image Segmentation (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Manish Joshi
    • 1
  • Pawan Lingras
    • 2
  1. 1.Department of Computer ScienceNorth Maharashtra UniversityJalgaonIndia
  2. 2.Department of Mathematics and Computing ScienceSaint Mary’s UniversityHalifaxCanada

Personalised recommendations