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A Hybrid CRO-K-Means Algorithm for Data Clustering

  • Sibarama Panigrahi
  • Balaram Rath
  • P. Santosh Kumar
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

Over the past few decades clustering algorithms have been used in diversified fields of engineering and science. Out of various methods, K-Means algorithm is one of the most popular clustering algorithms. However, K-Means algorithm has a major drawback of trapping to local optima. Motivated by this, this paper attempts to hybridize Chemical Reaction Optimization (CRO) algorithm with K-Means algorithm for data clustering. In this method K-Means algorithm is used as an on-wall ineffective collision reaction in the CRO algorithm, thereby enjoying the intensification property of K-Means algorithm and diversification of intermolecular reactions of CRO algorithm. The performance of the proposed methodology is evaluated by comparing the obtained results on four real world datasets with three other algorithms including K-Means algorithm, CRO-based and differential evolution (DE) based clustering algorithm. Experimental result shows that the performance of proposed clustering algorithm is better than K-Means, DE-based, CRO-based clustering algorithm on the datasets considered.

Keywords

Data clustering Chemical reaction optimization K-Means algorithm 

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Copyright information

© Springer India 2015

Authors and Affiliations

  • Sibarama Panigrahi
    • 1
  • Balaram Rath
    • 2
  • P. Santosh Kumar
    • 2
  1. 1.National Institute of Science and TechnologyBerhampurIndia
  2. 2.MIRC LabMITS Engineering CollegeRayagadaIndia

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