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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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    1. Mirkin B., 2001. "Eleven Ways to Look at the Chi-Squared Coefficient for Contingency Tables," The American Statistician, American Statistical Association, vol. 55, pages 111-120, May.
    2. Heungsun Hwang & Hec Montréal & William Dillon & Yoshio Takane, 2006. "An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents," Psychometrika, Springer;The Psychometric Society, vol. 71(1), pages 161-171, March.
    3. Chae, Seong S. & DuBien, Janice L. & Warde, William D., 2006. "A method of predicting the number of clusters using Rand's statistic," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3531-3546, August.
    4. Thomas Nocke & Heidrun Schumann & Uwe Böhm, 2004. "Methods for the visualization of clustered climate data," Computational Statistics, Springer, vol. 19(1), pages 75-94, February.
    5. Vichi, Maurizio & Kiers, Henk A. L., 2001. "Factorial k-means analysis for two-way data," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 49-64, July.
    6. Johann Kraus & Christoph Müssel & Günther Palm & Hans Kestler, 2011. "Multi-objective selection for collecting cluster alternatives," Computational Statistics, Springer, vol. 26(2), pages 341-353, June.
    7. Vichi, Maurizio & Saporta, Gilbert, 2009. "Clustering and disjoint principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3194-3208, June.
    8. Carlo Lauro & Simona Balbi, 1999. "The analysis of structured qualitative data," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 15(1), pages 1-27, March.
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    Cited by:

    1. 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.
    2. M. Velden & A. Iodice D’Enza & F. Palumbo, 2017. "Cluster Correspondence Analysis," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 158-185, March.
    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.
    4. Mario Musella & Ida Camminatiello & Francesco Izzo, 2024. "Caritas’s Work for the Goals of Agenda 2030: A Study on the Services Provided in Campania," Mathematics, MDPI, vol. 12(15), pages 1-17, July.

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