Automatic Clustering Based on Invasive Weed Optimization Algorithm

  • Aritra Chowdhury
  • Sandip Bose
  • Swagatam Das
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)


In this article, an evolutionary metaheuristic algorithm known as the Invasive Weed Optimization (IWO) is applied for automatically partitioning a dataset without any prior information about the number of naturally occurring groups in the data. The fitness function used in the genetic algorithm is a cluster validity index. Depending on the results of this index IWO returns the segmented dataset along with the appropriate number of divisions. The proficiency of this algorithm is compared to variable string length genetic algorithm with point symmetry based distance clustering(VGAPS-clustering), variable string length Genetic K-means algorithm(GCUK-clustering) and a weighted sum validity function based hybrid niching genetic algorithm(HNGA-clustering) and is denoted for the nine artificial datasets and four real life datasets.


Invasive Weed Optimization Clustering Cluster validity index Genetic Algorithm Variable number of clusters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bandyopadhyay, S., Saha, S.: A Point Symmetry Based Clustering Technique for Automatic Evaluation of Clusters. IEEE Transactions on Knowledge and Data Engineering 20(11) (November 2008)Google Scholar
  2. 2.
    Mehrabian, A.R., Lucas, C.: A Novel Optimization Algorithm Inspired from Weed Colonization. In: Ecological Informatics. Elsevier (2006)Google Scholar
  3. 3.
    Sepehri Rad, H., Lucas, C.: A Recommender System Based On Invasive Weed Optimization Algorithm. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 4297–4304 (2007)Google Scholar
  4. 4.
    Bandyopadhyay, S., Maulik, U.: Genetic Clustering for Automatic Evolution of Clusters and Application to Image Classification. Pattern Recognition (2), 1197–1208 (2002)Google Scholar
  5. 5.
    D.N.A. Asuncion: UCI Machine Learning Repository (2007) Google Scholar
  6. 6.
    Holland, J.H.: Adaptation in Natural and Artificial System. The University of Michigan Press, AnnArbor (1975)Google Scholar
  7. 7.
    Sheng, W., Swift, S., Zhang, L., Liu, X.: A Weighted Sum Validity Function for Clustering with a Hybrid Niching Genetic Algorithm. IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernatics 35(6) (December 2005)Google Scholar
  8. 8.
    Ben-Hur, A., Guyon, I.: Detecting Stable Clusters Using Principal Component Analysis in Methods of Molecular Biology. Humana Press (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aritra Chowdhury
    • 1
  • Sandip Bose
    • 1
  • Swagatam Das
    • 2
  1. 1.Dept. of Electronics and Telecomunication EnggJadavpur UniversityKolkataIndia
  2. 2.Electronics and Computer Sciences UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations