An Efficient Clustering Scheme for Cloud Computing Problems Using Parameter Improved Particle Swarm Optimization (PIPSO) Technique
Keywords:
Cloud Computing, Data clustering, PIPSO, ACO, PSO, Optimization based clustering.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 Parameter Improved Particle Swarm Optimization (PIPSO) algorithm. It is an optimization approach for data clustering problem, in which a swarm of particles (candidate solutions) moves to converge a specific positions as final cluster centres by minimizing the fitness function. The proposed method is compared with the common clustering methods such as k-means clustering algorithm, data clustering using particle swarm optimization algorithm, ant colony optimization based algorithm, and proposed clustering method using parameter improved particle swarm optimization algorithm, MATLAB simulation results evidence that the proposed technique gives better results when compared to other existing techniques. The proposed data clustering method can be employed to manipulate vast data sets with different cluster sizes, multi dimensional and densities.
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