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Hybrid metaheuristic algorithm for improving the efficiency of data clustering

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Abstract

Clustering is a technique which is used to group the data into different subgroups or subsets to retrieve meaningful information from the available huge dataset. The trending swarm based intelligent system replaces the conventional clustering algorithm with the objective of increased performance. Ant lion optimization (ALO) technique is the swarm based intelligence that exhibits the hunting mechanism of the ant lions in the natural environment. Ant colony optimization (ACO) algorithm is a swarm based intelligence technique which inherits the behaviour of natural ant. In this paper new hybrid ACO–ALO algorithm was proposed to solve the data clustering problem. Additionally Cauchy’s mutation operator is added with this proposed algorithm to avoid the local minima trapping problem. The main objective is to reduce the intra cluster distance in clustering problem. From the experimental analysis, it evidences the proposed ACO–ALO algorithm outperforms the traditional algorithms of data clustering.

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Authors and Affiliations

  1. Department of Information Technology, SNS College of Technology, Coimbatore, 641035, Tamilnadu, India

    C. Mageshkumar

  2. Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, 641035, Tamilnadu, India

    S. Karthik & V. P. Arunachalam

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  1. C. Mageshkumar
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  2. S. Karthik
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  3. V. P. Arunachalam
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Correspondence to C. Mageshkumar.

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Mageshkumar, C., Karthik, S. & Arunachalam, V.P. Hybrid metaheuristic algorithm for improving the efficiency of data clustering. Cluster Comput 22 (Suppl 1), 435–442 (2019). https://doi.org/10.1007/s10586-018-2242-8

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  • DOI: https://doi.org/10.1007/s10586-018-2242-8

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