K-means clustering using Tabu Search with quantized means

Kojo Sarfo Gyamfi, James Brusey, Andrew Hunt

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

The Tabu Search (TS) metaheuristic has been proposed for K-Means clustering as an alternative to Lloyd's algorithm, which for all its ease of implementation and fast runtime, has the major drawback of being trapped at local optima. While the TS approach can yield superior performance, it involves a high computational complexity. Moreover, the difficulty in parameter selection in the existing TS approach does not make it any more attractive. This paper presents an alternative, low-complexity formulation of the TS optimization procedure for K-Means clustering. This approach does not require many parameter settings. We initially constrain the centers to points in the dataset. We then aim at evolving these centers using a unique neighborhood structure that makes use of gradient information of the objective function. This results in an efficient exploration of the search space, after which the means are refined. The proposed scheme is implemented in MATLAB and tested on four real-world datasets, and it achieves a significant improvement over the existing TS approach in terms of the intra cluster sum of squares and computational time.

Original languageEnglish
Title of host publicationWCECS 2016 - World Congress on Engineering and Computer Science 2016
PublisherNewswood Limited
Pages426-432
Number of pages7
Volume2225
ISBN (Electronic)9789881404718
Publication statusPublished - 2016
Event2016 World Congress on Engineering and Computer Science, WCECS 2016 - San Francisco, United States
Duration: 19 Oct 201621 Oct 2016

Other

Other2016 World Congress on Engineering and Computer Science, WCECS 2016
CountryUnited States
CitySan Francisco
Period19/10/1621/10/16

Keywords

  • clustering
  • K-means
  • Tabu search
  • unsupervised learning

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