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Cluster Computing

, Volume 22, Supplement 5, pp 12917–12927 | Cite as

An efficient clustering scheme for cloud computing problems using metaheuristic algorithms

  • K. M. BaalamuruganEmail author
  • S. Vijay Bhanu
Article

Abstract

Data clustering partitions the information into helpful classes or groups with no earlier learning. This is a fundamental method in the field of computer data mining and it has turned into an essential element in many other engineering areas including cloud computing. This paper purports a novel clustering technique based on the application of krill herd Efficient Stud Krill Herd—Clustering (ESKH-C) technique. It is an optimisation approach for data clustering problem in which a swarm of krill (candidate solutions) moves to converge to specific positions as final cluster centres by minimizing the fitness function. The accuracy of the purposed methodology is blazed on different well familiar bench mark data sets. Analysed with the common clustering methods such as k-means clustering algorithm, data clustering using particle swarm optimization algorithm, ant colony optimization based data clustering, and clustering method using bacterial foraging algorithm, MATLAB simulation results evidence that the proposed technique is an effectual data clustering method. The proposed data clustering method can be employed to manipulate vast data sets with different cluster sizes, multi dimensional and densities.

Keywords

Cloud computing Data clustering Krill herd technique Optimization based clustering 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityChidambaramIndia

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