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)


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.


Data clustering Chemical reaction optimization K-Means algorithm 


  1. 1.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264–323 (1999)CrossRefGoogle Scholar
  2. 2.
    Jain, S., Gajbhiye, S.: A Comparative performance analysis of clustering algorithms. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2, 441–445 (2012)Google Scholar
  3. 3.
    Abbas, O.A.: Comparisons between data clustering algorithms. Int. Arab J. Inf. Technol. 5, 320–325 (2008)Google Scholar
  4. 4.
    Dash, R., Mishra, D., Rath, A.K., Acharya, M.: A hybridized K-means clustering algorithm for high dimensional dataset. Int. J. Comput. Sci. Technol. 2, 59–66 (2010)Google Scholar
  5. 5.
    Yedla, M., Pathakota, S.R., Srinivasa, T.M.: Enhancing K means algorithm with improved initial center. Int. J. Comput. Sci. Inf. Technol. 1, 121–125 (2010)Google Scholar
  6. 6.
    Fahim, A.M., Salem, A.M., Torkey, F.A., Ramadan, M.A., Saake, G.: An efficient K-means with good initial starting points. Georgian Electron. Sci. J. Comput. Sci. Telecommun. 2, 47–57 (2009)Google Scholar
  7. 7.
    Zhang, C., Xia, S.: K-means clustering algorithm with improved initial center. In: Second International Workshop on Knowledge Discovery and Data Mining, WKDD, pp. 790–792 (2009)Google Scholar
  8. 8.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)CrossRefMathSciNetMATHGoogle Scholar
  9. 9.
    Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  10. 10.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  11. 11.
    Socha, K., Doringo, M.: Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185, 1155–1173 (2008)CrossRefMATHGoogle Scholar
  12. 12.
    Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm—a novel tool for complex optimization problems. In: Proceedings of 2nd International Virtual Conference on Intelligent Production Machines and Systems, pp. 454–459 (2006)Google Scholar
  13. 13.
    Beyer, H.G., Schwefel, H.P.: Evolutionary strategies: a comprehensive introduction. Nat. Comput. 1, 3–52 (2002)CrossRefMathSciNetMATHGoogle Scholar
  14. 14.
    Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6, 580–593 (2002)CrossRefGoogle Scholar
  15. 15.
    Lam, A.Y.S., Li, V.O.K.: Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans. Evol. Comput. 14, 381–399 (2010)CrossRefGoogle Scholar
  16. 16.
    Lam, A.Y.S.: Real-coded chemical reaction optimization. IEEE Trans. Evol. Comput. 16, 339–353 (2012)CrossRefGoogle Scholar
  17. 17.
    Alatas, B.: ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38, 13170–13180 (2011)CrossRefGoogle Scholar
  18. 18.
    Al-Shboul, B., Myaeng, S.: Initializing K-means clustering algorithm by using genetic algorithm. World Acad. Sci. Eng. Technol. 54, 114–118 (2009)Google Scholar
  19. 19.
    Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recogn. 33, 1455–1465 (2000)CrossRefGoogle Scholar
  20. 20.
    Bandyopadhyay, S., Maulik, U.: An evolutionary technique based on K-means algorithm for optimal clustering in RN. Inf. Sci. 146, 221–237 (2002)CrossRefMathSciNetMATHGoogle Scholar
  21. 21.
    Paterlini, S., Krink, T.: High performance clustering with differential evolution. evolutionary computation. In: CEC2004, vol. 2, pp. 2004–2011 (2004)Google Scholar
  22. 22.
    Satapathy, S.C., Naik, A.: Data clustering based on teaching-learning-based optimization. In: Swarm, Evolutionary and Memetic Computing Conference Part II, LNCS, vol. 7077, pp. 148–156 (2011)Google Scholar
  23. 23.
    Naik, A., Satapathy, S.C., Parvathi, K.: Improvement of initial cluster center of c-means using teaching learning based optimization. Procedia Technol. 6, 428–435 (2012)CrossRefGoogle Scholar
  24. 24.
    Das, S., Suganthanam, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31 (2011)CrossRefGoogle Scholar
  25. 25.
    Sahu, K.K., Panigrahi, S., Behera, H.S.: A novel chemical reaction optimization algorithm for higher order neural network training. J. Theoret. Appl. Inf. Technol. 53, 402–409 (2013)Google Scholar
  26. 26.
    Baral, A., Behera, H.S.: A novel chemical reaction-based clustering and its performance analysis. Int. J. Bus. Intell. Data Min. 8, 184–198 (2013)CrossRefGoogle Scholar

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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