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Clustering Methods for Ordinal Data: A Comparison Between Standard and New Approaches

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Advances in Statistical Models for Data Analysis

Abstract

The literature on cluster analysis has a long and rich history in several different fields. In this paper, we provide an overview of the more well-known clustering methods frequently used to analyse ordinal data. We summarize and compare their main features discussing some key issues. Finally, an example of application to real data is illustrated comparing and discussing clustering performances of different methods.

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Author information

Authors and Affiliations

  1. Department of Statistics, The Pennsylvania State University, University Park, USA

    Monia Ranalli

  2. Dipartimento IGF, Università di Roma Tor Vergata, Roma, Italy

    Roberto Rocci

Authors
  1. Monia Ranalli
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  2. Roberto Rocci
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Correspondence to Monia Ranalli .

Editor information

Editors and Affiliations

  1. Department of Economics "Marco Biagi", University of Modena & Reggio Emilia, Modena, Italy

    Isabella Morlini

  2. Department of Communication and Economics, University of Modena & Reggio Emilia, Reggio Emilia, Italy

    Tommaso Minerva

  3. Department of Statistics, University of Rome “La Sapienza”, Roma, Italy

    Maurizio Vichi

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Ranalli, M., Rocci, R. (2015). Clustering Methods for Ordinal Data: A Comparison Between Standard and New Approaches. In: Morlini, I., Minerva, T., Vichi, M. (eds) Advances in Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-17377-1_23

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