A New Hybrid Approach for Document Clustering Using Tabu Search and Particle Swarm Optimization (TSPSO)

  • T. Haribabu
  • S. Jayaprada
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)


Clustering of text documents is the quickest developing research area, because of the availability of vast amount of information in an electronic form. To solve this document cluster analysis difficulties more efficiently and quickly, this paper proposes a hybrid method using tabu search particle swarm optimization (TSPSO). First, the automatic merging optimization clustering (AMOC) algorithm was performed for the formation of clusters and then implemented the optimization model using the variance ratio criterion (VRC) as fitness function .Second, this paper combines TS and PSO algorithm to use the exploration of both algorithms and to avoid flaws of both algorithms .The testing of TSPSO algorithm is performed on several standard datasets, and the results are compared with PSO and TS. So, the proposed TSPSO is efficient and effective for the problem of document clustering; we have tested PSO, TS, and our proposed TSPSO algorithm on various text document collections.


Clustering Particle swarm optimization Tabu search 


  1. 1.
    Jaganathan, P., Jaiganesh, S.: An improved K-means algorithm combined with Particle Swarm Optimization approach for efficient web document clustering. In: International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), IEEE (2013) Google Scholar
  2. 2.
    Yaghini, M., Ghazanfari, N.: Tabu-KM: a hybrid clustering algorithm based on tabu search approach. Int. J. Ind. Eng. Prod. Res. 21, 71–79 (2010)Google Scholar
  3. 3.
    Vora, P., Oza, B.: A survey on K-mean clustering and particle swarm optimization. Int. J. Sci. Mod. Eng. (IJISME) 1(3), 24–26 (2013). ISSN: 2319-6386Google Scholar
  4. 4.
    Zhang, Y., Li, D.: Cluster analysis by variance ratio criterion and firefly algorithm. Int. J. Digital Content Technol. Appl. (JDCTA) 7(3), 689 (2013)Google Scholar
  5. 5.
    karypis, G.: CLUTO a clustering Toolkit. Technical report, Department of Computer Science, University .of Minnesota (2013).
  6. 6.
    Premalatha, K., Natarajan, A.M.: Discrete PSO with GA operators for document clustering. Int. J. Recent Trends Eng. 1(1), 20–24 (2009)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia

Personalised recommendations