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A Generalized Study on Data Mining and Clustering Algorithms

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

Author

Listed:
  • Syed Thouheed Ahmed

    (Dr. T. Thimmaiah Institute of Technology, Department of Computer Science and Engineering)

  • S. Sreedhar Kumar

    (Dr. T. Thimmaiah Institute of Technology, Department of Computer Science and Engineering)

  • B. Anusha

    (Dr. T. Thimmaiah Institute of Technology, Department of Computer Science and Engineering)

  • P. Bhumika

    (Dr. T. Thimmaiah Institute of Technology, Department of Computer Science and Engineering)

  • M. Gunashree

    (Dr. T. Thimmaiah Institute of Technology, Department of Computer Science and Engineering)

  • B. Ishwarya

    (Dr. T. Thimmaiah Institute of Technology, Department of Computer Science and Engineering)

Abstract

Data Mining is the procedure of extracting information from a data set and transforms information into comprehensible structure for processing. Clustering is data mining technique used to process data elements into their related groups or partition. Thus, the process of partitioning data objects into subclasses is term as ‘cluster’. It consists of data objects with un-unified proposition of high inter similarity and low intra similarity ratios. Thus reflecting the quality of cluster depends on the methods used. Clustering also called data segmentation, divides huge data sets into several groups based upon their similarities. This paper discusses a literature study of various clustering techniques and their comparison on key issues to give guidance for choosing clustering algorithm for a specific research application. The comparison is based on computing performance and clustering accuracy.

Suggested Citation

  • Syed Thouheed Ahmed & S. Sreedhar Kumar & B. Anusha & P. Bhumika & M. Gunashree & B. Ishwarya, 2020. "A Generalized Study on Data Mining and Clustering Algorithms," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1121-1129, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_114
    DOI: 10.1007/978-3-030-41862-5_114
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