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Chemical Reaction-Based Optimization Algorithm for Solving Clustering Problems

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

Abstract

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.

Keywords

Artificial chemical reaction optimization Clustering Meta-heuristic algorithms Chemical reaction 

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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

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