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Cluster Computing
Published in: Cluster Computing 1/2019

24-02-2018

Hybrid metaheuristic algorithm for improving the efficiency of data clustering

Authors: C. Mageshkumar, S. Karthik, V. P. Arunachalam

Published in: Cluster Computing | Special Issue 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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Metadata
Title
Hybrid metaheuristic algorithm for improving the efficiency of data clustering
Authors
C. Mageshkumar
S. Karthik
V. P. Arunachalam
Publication date
24-02-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 1/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2242-8

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