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Cluster Analysis: Modeling Groups in Text

  • Murugan Anandarajan
  • Chelsey Hill
  • Thomas Nolan
Chapter
Part of the Advances in Analytics and Data Science book series (AADS, volume 2)

Abstract

This chapter explains the unsupervised learning method of grouping data known as cluster analysis. The chapter shows how hierarchical and k-means clustering can place text or documents into significant groups to increase the understanding of the data. Clustering is a valuable tool that helps us find naturally occurring similarities.

Keywords

Cluster analysis Hierarchical cluster analysis k-means cluster analysis k-means Single linkage Complete linkage Centroid Ward’s method 

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Further Reading

  1. For more about clustering, see Berkhin (2006), Jain and Dubes (1988) and Jain et al. (1999).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Murugan Anandarajan
    • 1
  • Chelsey Hill
    • 2
  • Thomas Nolan
    • 3
  1. 1.LeBow College of BusinessDrexel UniversityPhiladelphiaUSA
  2. 2.Feliciano School of BusinessMontclair State UniversityMontclairUSA
  3. 3.Mercury Data ScienceHoustonUSA

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