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Comparative Analysis of Evolutionary Approaches and Computational Methods for Optimization in Data Clustering

In: New Trends in Computational Vision and Bio-inspired Computing

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  • Anuradha D. Thakare

    (Pimpri Chinchwad College of Engineering, Department of Computer Engineering)

Abstract

Clustering is an essential step to discover the actionable information from complicated search space. In the era of digitization, the need to identify and structure this actionable information has made clustering one of the potential research areas. The traditional clustering models results into local optima, as clustering results confines to selection of initial seeds. Therefore, the computational models with heuristic search approach are required to get optimal clusters. This paper presents a review of the various approaches for research in data clustering. It describes the advancements achieved in the area of data clustering using evolutionary approaches and briefly traces the progress made to the clustering problem. Analysis of existing approaches is presented with critical remarks. Summary and comparison of related work are discussed. Finally, paper closes with a summary that leads to the issues and challenges for future research.

Suggested Citation

  • Anuradha D. Thakare, 2020. "Comparative Analysis of Evolutionary Approaches and Computational Methods for Optimization in Data Clustering," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 587-593, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_57
    DOI: 10.1007/978-3-030-41862-5_57
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