Chemical Reaction-Based Optimization Algorithm for Solving Clustering Problems

  • Yugal Kumar
  • Neeraj Dahiya
  • Sanjay Malik
  • Geeta Yadav
  • Vijendra Singh
Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)


Heuristic algorithms are widely used in the diverse fields of engineering and sciences and prove its efficiency over classical algorithms. In the analysis of chemical process, it is observed that the formation of new product consists of a proficient computational procedure among chemical reactions. These chemical reactions consist of objects, events, states, and process. In this work, an efficient and robust algorithm, called artificial chemical reaction optimization algorithm, is adopted for solving the partitional clustering problems. The performance of the proposed algorithm is investigated on well-known clustering datasets. Further, the simulation results of the CRO-based clustering algorithm are compared with some state-of-the-art clustering algorithms. It is seen that proposed clustering algorithm provides better performance than other algorithms in terms of intra-cluster distance and f-measure.


Artificial chemical reaction optimization Clustering Meta-heuristic algorithms Chemical reaction 


  1. 1.
    Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38(10):13170–13180CrossRefGoogle Scholar
  2. 2.
    Alatas B (2012) A novel chemistry based metaheuristic optimization method for mining of classification rules. Expert Syst Appl 39(12):11080–11088CrossRefGoogle Scholar
  3. 3.
    Anaya AR, Boticario JG (2011) Application of machine learning techniques to analyses student interactions and improve the collaboration process. Expert Syst Appl 38(2):1171–1181CrossRefGoogle Scholar
  4. 4.
    Dunn WJ III, Greenberg MJ, Callejas SS (1976) Use of cluster analysis in the development of structure-activity relations for antitumor triazenes. J Med Chem 19(11):1299–1301CrossRefGoogle Scholar
  5. 5.
    Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Applic 22(6):1239–1255CrossRefGoogle Scholar
  6. 6.
    Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inform Sci 222:175–184MathSciNetCrossRefGoogle Scholar
  7. 7.
    He Y, Pan W, Lin J (2006) Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data. Comput Stat Data Anal 51(2):641–658MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hung YS, Chen KLB, Yang CT, Deng GF (2013) Web usage mining for analyzing elder self-care behavior patterns. Expert Syst Appl 40(2):775–783CrossRefGoogle Scholar
  9. 9.
    Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3):267–289CrossRefGoogle Scholar
  10. 10.
    Kaveh A, Share MAM, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224(1):85–107CrossRefGoogle Scholar
  11. 11.
    Kumar Y, Sahoo G (2014) A charged system search approach for data clustering. Prog Artif Intell 2(2-3):153–166CrossRefGoogle Scholar
  12. 12.
    Kumar Y, Sahoo G (2015) Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy. Soft Comput 19(12):3621–3645CrossRefGoogle Scholar
  13. 13.
    Kumar Y, Sahoo G (2015) A hybrid data clustering approach based on improved cat swarm optimization and K-harmonic mean algorithm. AI Commun 28(4):751–764MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kumar Y, Sahoo G (2016) A hybridize approach for data clustering based on cat swarm optimization. Int. J Inf Commun Technol 9(1):117–141MathSciNetGoogle Scholar
  15. 15.
    MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley Symposium on Mathematics. Statistics and Probability. University of California Press, pp 281–297Google Scholar
  16. 16.
    Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(1-2):132–140Google Scholar
  17. 17.
    Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Analy Chim Acta 509(2):187–195CrossRefGoogle Scholar
  18. 18.
    Teppola P, Mujunen SP, Minkkinen P (1999) Adaptive Fuzzy C-Means clustering in process monitoring. Chemom Intel Lab Syst 45(1):23–38CrossRefGoogle Scholar
  19. 19.
    Webb A (2002) Statistical pattern recognition. Wiley, New Jersey, pp 361–406zbMATHGoogle Scholar
  20. 20.
    Zhan ZH, Zhang J, LiY CSH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B Cybern 39:1362–1381CrossRefGoogle Scholar
  21. 21.
    Zhou H, Liu Y (2008) Accurate integration of multi-view range images using k-means clustering. Pattern Recognit 41(1):152–175CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Yugal Kumar
    • 1
  • Neeraj Dahiya
    • 2
  • Sanjay Malik
    • 2
  • Geeta Yadav
    • 3
  • Vijendra Singh
    • 4
  1. 1.Department of Computer Science and Engineering, JUITWaknaghatIndia
  2. 2.Department of Computer Science and EngineeringSRM University, Delhi-NCR CampusGhaziabadIndia
  3. 3.Department of PharmacyManav Bharti UniversitySolanIndia
  4. 4.Department of Computer Science and EngineeringThe Northcap UniversityGurugramIndia

Personalised recommendations