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Cluster Correspondence Analysis

Author

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  • van de Velden, M.
  • Iodice D' Enza, A.
  • Palumbo, F.

Abstract

__Abstract__ A new method is proposed that combines dimension reduction and cluster analysis for categorical data. A least-squares objective function is formulated that approximates the cluster by variables cross-tabulation. Individual observations are assigned to clusters in such a way that the distributions over the categorical variables for the different clusters are optimally separated. In a unified framework, a brief review of alternative methods is provided and performance of the methods is appraised by means of a simulation study. The results of the joint dimension reduction and clustering methods are compared with cluster analysis based on the full dimensional data. Our results show that the joint dimension reduction and clustering methods outperform, both with respect to the retrieval of the true underlying cluster structure and with respect to internal cluster validity measures, full dimensional clustering. The differences increase when more variables are involved and in the presence of noise variables.

Suggested Citation

  • 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.
    • Handle: RePEc:ems:eureir:77010
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    References listed on IDEAS

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    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. 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.
    3. Michel Velden & Yoshio Takane, 2012. "Generalized canonical correlation analysis with missing values," Computational Statistics, Springer, vol. 27(3), pages 551-571, September.
    4. Alfonso Iodice D’Enza & Francesco Palumbo, 2013. "Iterative factor clustering of binary data," Computational Statistics, Springer, vol. 28(2), pages 789-807, April.
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    Cited by:

    1. Ming Sun & Ronggui Zhou, 2023. "Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis," Sustainability, MDPI, vol. 15(12), pages 1-21, June.

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    Keywords

    Correspondence analysis; cluster analysis; dimension; reduction; categorical variables;
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