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A hybrid ACO-GRASP algorithm for clustering analysis

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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.

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Authors and Affiliations

  1. Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete, 73100, Chania, Greece

    Yannis Marinakis & Nikolaos Matsatsinis

  2. Industrial Systems Control Laboratory, Department of Production Engineering and Management, Technical University of Crete, 73100, Chania, Greece

    Magdalene Marinaki

  3. Financial Engineering Laboratory, Department of Production Engineering and Management, Technical University of Crete, 73100, Chania, Greece

    Michael Doumpos & Constantin Zopounidis

Authors
  1. Yannis Marinakis
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  2. Magdalene Marinaki
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  3. Michael Doumpos
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  4. Nikolaos Matsatsinis
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  5. Constantin Zopounidis
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Correspondence to Constantin Zopounidis.

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Marinakis, Y., Marinaki, M., Doumpos, M. et al. A hybrid ACO-GRASP algorithm for clustering analysis. Ann Oper Res 188, 343–358 (2011). https://doi.org/10.1007/s10479-009-0519-2

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