A WK-Means approach for clustering

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Clustering is an unsupervised learning method that is used to group similar objects. One of the most popular and efficient clustering methods is K-means, as it has linear time complexity and is simple to implement. However, it suffers from gets trapped in local optima. Therefore, many methods have been produced by hybridizing K-means and other methods. In this paper, we propose a hybrid method that hybridizes Invasive Weed Optimization (IWO) and K-means. The IWO algorithm is a recent population based method to iteratively improve the given population of a solution. In this study, the algorithm is used in the initial stage to generate a good quality solution for the second stage. The solutions generated by the IWO algorithm are used as initial solutions for the K-means algorithm. The proposed hybrid method is evaluated over several real world instances and the results are compared with well-known clustering methods in the literature. Results show that the proposed method is promising compared to other methods.

Original languageEnglish
Pages (from-to)489-493
Number of pages5
JournalInternational Arab Journal of Information Technology
Volume12
Issue number5
Publication statusPublished - 1 Sep 2015

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Unsupervised learning

Keywords

  • Data clustering
  • Hybrid evolutionary optimization algorithm
  • IWO
  • K-means algorithm
  • Unsupervised learning

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

A WK-Means approach for clustering. / Boobord, Fatemeh; Othman, Zalinda; Abu Bakar, Azuraliza.

In: International Arab Journal of Information Technology, Vol. 12, No. 5, 01.09.2015, p. 489-493.

Research output: Contribution to journalArticle

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