Hybridization of K-means and harmony search methods for web page clustering

Rana Forsati, Mohammad Reza Meybodi, Mehrdad Mahdavi, Azadeh Ghari Neiat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations

Abstract

Clustering is currently one of the most crucial techniques for dealing with massive amount of heterogeneous information on the web, which is beyond human being's capacity to digest. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we present novel harmony search clustering algorithms that deal with documents clustering based on harmony search optimization method. By modeling clustering as an optimization problem, first, we propose a pure harmony search based clustering algorithm that finds near global optimal clusters within a reasonable time. Contrary to the localized searching of the K-means algorithm, the harmony search clustering algorithm performs a globalized search in the entire solution space. Then harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering. The proposed algorithms improve the K-means algorithm by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, hence more stable. In the experiments we conducted, we applied the proposed algorithms, K-means clustering algorithm on five different document datasets. Experimental results reveal that the proposed algorithms can find better clusters when compared to K-means and the quality of clusters is comparable and converge to the best known optimum faster than it.

Original languageEnglish (US)
Title of host publicationProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Pages329-335
Number of pages7
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 - Sydney, NSW, Australia
Duration: Dec 9 2008Dec 12 2008

Publication series

NameProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008

Other

Other2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
CountryAustralia
CitySydney, NSW
Period12/9/0812/12/08

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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    Forsati, R., Meybodi, M. R., Mahdavi, M., & Neiat, A. G. (2008). Hybridization of K-means and harmony search methods for web page clustering. In Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 (pp. 329-335). [4740468] (Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008). https://doi.org/10.1109/WIIAT.2008.370