Novel meta-heuristic algorithms for clustering web documents

M. Mahdavi, M. Haghir Chehreghani, H. Abolhassani, R. Forsati

Research output: Contribution to journalArticlepeer-review

84 Scopus citations

Abstract

Clustering the web documents is one of the most important approaches for mining and extracting knowledge from the web. Recently, one of the most attractive trends in clustering the high dimensional web pages has been tilt toward the learning and optimization approaches. In this paper, we propose novel hybrid harmony search (HS) based algorithms for clustering the web documents that finds a globally optimal partition of them into a specified number of clusters. 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. Then, we hybridize K-means and harmony clustering in two ways to achieve better clustering. Experimental results reveal that the proposed algorithms can find better clusters when compared to similar methods and also illustrate the robustness of the hybrid clustering algorithms.

Original languageEnglish (US)
Pages (from-to)441-451
Number of pages11
JournalApplied Mathematics and Computation
Volume201
Issue number1-2
DOIs
StatePublished - Jul 15 2008

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

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