Iterative factor clustering of binary data

Computational Statistics volume 28, pages789–807(2013)Cite this article

Abstract

Binary data represent a very special condition where both measures of distance and co-occurrence can be adopted. Euclidean distance-based non-hierarchical methods, like the k-means algorithm, or one of its versions, can be profitably used. When the number of available attributes increases the global clustering performance usually worsens. In such cases, to enhance group separability it is necessary to remove the irrelevant and redundant noisy information from the data. The present approach belongs to the category of attribute transformation strategy, and combines clustering and factorial techniques to identify attribute associations that characterize one or more homogeneous groups of statistical units. Furthermore, it provides graphical representations that facilitate the interpretation of the results.

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Affiliations

  1. Dipartimento di Scienze Economiche, Università di Cassino, Cassino, Italy

    Alfonso Iodice D’Enza

  2. Dipartimento di Teoria e Metodi per le Scienze Umane e Sociali, Università degli Studi di Napoli ‘Federico II’, Naples, Italy

    Francesco Palumbo

Authors
  1. Alfonso Iodice D’Enza
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  2. Francesco Palumbo
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Correspondence to Francesco Palumbo.

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Iodice D’Enza, A., Palumbo, F. Iterative factor clustering of binary data. Comput Stat 28, 789–807 (2013). https://doi.org/10.1007/s00180-012-0329-x

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Keywords

  • Categorical attribute quantification
  • Correspondence analysis
  • Cluster analysis
  • Binary data