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Iterative factor clustering of binary data

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  • Alfonso Iodice D’Enza
  • Francesco Palumbo

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. Copyright Springer-Verlag 2013

Suggested Citation

  • Alfonso Iodice D’Enza & Francesco Palumbo, 2013. "Iterative factor clustering of binary data," Computational Statistics, Springer, vol. 28(2), pages 789-807, April.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:2:p:789-807
    DOI: 10.1007/s00180-012-0329-x
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    References listed on IDEAS

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    Cited by:

    1. M. Velden & A. Iodice D’Enza & F. Palumbo, 2017. "Cluster Correspondence Analysis," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 158-185, March.
    2. Masaki Mitsuhiro & Hiroshi Yadohisa, 2015. "Reduced $$k$$ k -means clustering with MCA in a low-dimensional space," Computational Statistics, Springer, vol. 30(2), pages 463-475, June.
    3. van de Velden, M. & Iodice D' Enza, A. & Palumbo, F., 2014. "Cluster Correspondence Analysis," Econometric Institute Research Papers EI 2014-24, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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