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Performance of Internal Cluster Validations Measures For Evolutionary Clustering

  • Pranav Nerurkar
  • Aruna Pavate
  • Mansi Shah
  • Samuel Jacob
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

Clustering is an NP-hard grouping problem and thus there are advantages of using a metaheuristic (swarm intelligence) strategy to find the near global optimal solution to it. To effectively guide the agents of the swarm in the metaheuristic strategy, a suitable cost function is needed for successful outcome. The current inquiry focuses on the use of internal validation criteria as cost functions as they achieve the dual goals of clustering which are compactness and separation. Out of the multiple internal validation criteria included in the literature, two are identified for this purpose, viz. BetaCV and Dunn index. These were used as cost functions of the swarm optimizer metaheuristic (PSO-BCV and PSO-Dunn). To demonstrate the validity of the proposed technique, it was compared with other metaheuristics differential evolution as well as the traditional swarm optimizer based on distance-based criteria (PSO). The analysis of the results obtained on clustering benchmark datasets highlighted the suitability of this approach.

Keywords

Evolutionary clustering Swarm intelligence Cluster analysis Cluster validation Optimization 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Pranav Nerurkar
    • 1
  • Aruna Pavate
    • 2
  • Mansi Shah
    • 3
  • Samuel Jacob
    • 4
  1. 1.Department of CE & ITVJTIMumbaiIndia
  2. 2.Department of CE & ITAtharva CoEMumbaiIndia
  3. 3.Department of CE & ITRizvi CoEMumbaiIndia
  4. 4.Jagdishprasad Jhabarmal Tibrewala University, JhunjhunuRajasthanIndia

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