Cluster and Discriminant Analysis

S. P. Mukherjee (), Bikas K. Sinha and Asis Kumar Chattopadhyay ()
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S. P. Mukherjee: University of Calcutta, Department of Statistics
Bikas K. Sinha: Indian Statistical Institute
Asis Kumar Chattopadhyay: University of Calcutta, Department of Statistics

Chapter Chapter 8 in Statistical Methods in Social Science Research, 2018, pp 75-94 from Springer

Abstract: 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.

Keywords: Clustering; Distance; Proximity; Similarity; Dissimilarity; Linkage; Classification; Multivariate Normal; Discriminator; Linear discrimination function (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-981-13-2146-7_8

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