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Visualization and clustering of categorical data with probabilistic self-organizing map

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Abstract

This paper introduces a self-organizing map dedicated to clustering, analysis and visualization of categorical data. Usually, when dealing with categorical data, topological maps use an encoding stage: categorical data are changed into numerical vectors and traditional numerical algorithms (SOM) are run. In the present paper, we propose a novel probabilistic formalism of Kohonen map dedicated to categorical data where neurons are represented by probability tables. We do not need to use any coding to encode variables. We evaluate the effectiveness of our model in four examples using real data. Our experiments show that our model provides a good quality of results when dealing with categorical data.

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Authors and Affiliations

  1. University of Paris 13, LIPN-UMR 7030, CNRS, 99, av. J-B Clément, 93430, Villetaneuse, France

    Mustapha Lebbah

  2. University of Lyon 1, LIESP EA4125, 69622, Lyon, France

    Khalid Benabdeslem

Authors
  1. Mustapha Lebbah
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  2. Khalid Benabdeslem
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Correspondence to Khalid Benabdeslem.

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Lebbah, M., Benabdeslem, K. Visualization and clustering of categorical data with probabilistic self-organizing map. Neural Comput & Applic 19, 393–404 (2010). https://doi.org/10.1007/s00521-009-0299-2

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