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Cluster Computing

Erschienen in:

24.02.2018

Hybrid metaheuristic algorithm for improving the efficiency of data clustering

verfasst von: C. Mageshkumar, S. Karthik, V. P. Arunachalam

Erschienen in: Cluster Computing | Sonderheft 1/2019

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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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Titel: Hybrid metaheuristic algorithm for improving the efficiency of data clustering
verfasst von: C. Mageshkumar
S. Karthik
V. P. Arunachalam
Publikationsdatum: 24.02.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 1/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-2242-8

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