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A hybrid ACO-GRASP algorithm for clustering analysis

Author

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  • Yannis Marinakis
  • Magdalene Marinaki
  • Michael Doumpos
  • Nikolaos Matsatsinis
  • Constantin Zopounidis

Abstract

Cluster analysis is an important tool for data exploration and it has been applied in a wide variety of fields like engineering, economics, computer sciences, life and medical sciences, earth sciences and social sciences. The typical cluster analysis consists of four steps (i.e. feature selection or extraction, clustering algorithm design or selection, cluster validation and results interpretation) with feedback pathway. These steps are closely related to each other and affect the derived clusters. In this paper, a new metaheuristic algorithm is proposed for cluster analysis. This algorithm uses an Ant Colony Optimization to feature selection step and a Greedy Randomized Adaptive Search Procedure to clustering algorithm design step. The proposed algorithm has been applied with very good results to many data sets. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Yannis Marinakis & Magdalene Marinaki & Michael Doumpos & Nikolaos Matsatsinis & Constantin Zopounidis, 2011. "A hybrid ACO-GRASP algorithm for clustering analysis," Annals of Operations Research, Springer, vol. 188(1), pages 343-358, August.
    • Handle: RePEc:spr:annopr:v:188:y:2011:i:1:p:343-358:10.1007/s10479-009-0519-2
    DOI: 10.1007/s10479-009-0519-2
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    References listed on IDEAS

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    1. Azzag, Hanene & Venturini, Gilles & Oliver, Antoine & Guinot, Christiane, 2007. "A hierarchical ant based clustering algorithm and its use in three real-world applications," European Journal of Operational Research, Elsevier, vol. 179(3), pages 906-922, June.
    2. Paterlini, Sandra & Krink, Thiemo, 2006. "Differential evolution and particle swarm optimisation in partitional clustering," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1220-1247, March.
    3. Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
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    Cited by:

    1. Behnam Tavakkol & Myong K. Jeong & Susan L. Albin, 2021. "Validity indices for clusters of uncertain data objects," Annals of Operations Research, Springer, vol. 303(1), pages 321-357, August.
    2. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.

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