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Cluster and Discriminant Analysis

In: Statistical Methods in Social Science Research

Author

Listed:
  • S. P. Mukherjee

    (University of Calcutta, Department of Statistics)

  • Bikas K. Sinha

    (Indian Statistical Institute)

  • Asis Kumar Chattopadhyay

    (University of Calcutta, Department of Statistics)

Abstract

Clustering can be considered to be the most important unsupervised learning technique to find homogeneous groups in a collection of a moderately large number of data points. Clustering could be defined as the process of dividing items into unknown number of groups whose members are alike in some way. A cluster is therefore a collection of items those are similar among themselves and are dissimilar to the items belonging to other clusters. It can be shown that there is no absolute "best" criterion which would be independent of the final aim of the clustering. Hence, the structure of the clusters should be finalized by the user depending on the physical requirements. By depending on the nature of analysis, clustering is called an unsupervised learning method and classification is called a supervised learning method.

Suggested Citation

  • S. P. Mukherjee & Bikas K. Sinha & Asis Kumar Chattopadhyay, 2018. "Cluster and Discriminant Analysis," Springer Books, in: Statistical Methods in Social Science Research, chapter 0, pages 75-94, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-2146-7_8
    DOI: 10.1007/978-981-13-2146-7_8
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