Abstract
There exist several methods for clustering high-dimensional data. One popular approach is to use a two-step procedure. In the first step, a dimension reduction technique is used to reduce the dimensionality of the data. In the second step, cluster analysis is applied to the data in the reduced space. This method may be referred to as the tandem approach. An important drawback of this method is that the dimension reduction may distort or hide the cluster structure. As an alternative, various authors have proposed joint dimension reduction and clustering approaches. In this paper we review some of these existing joint dimension reduction and clustering methods for categorical data in a unified framework that facilitates comparison.
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References
Arabie, P., & Hubert, L. (1994). Cluster analysis in marketing research. IEEE Transactions on Automatic Control, 19, 716–723.
Gifi, A. (1990). Nonlinear multivariate analysis. (579 pp). New York: John Wiley & Sons. ISBN 0-471-92620-5.
Hwang, H., Dillon, W. R., & Takane, Y. (2006). An extension of multiple correspondence analysis for identifying heterogenous subgroups of respondents. Psychometrika, 71, 161–171.
Iodice D’ Enza, A., & Palumbo, F. (2013). Iterative factor clustering of binary data. Computational Statistics, 28(2), 789–807.
Lauro C. N., & D’Ambra, L. (1984). L’analyse non symétrique des correspondances. Data Analysis and Informatics, III, 433–446.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. L. Cam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297).
Nenadic, O., & Greenacre, M. (2007). Correspondence analysis in R, with two- and three-dimensional graphics: the ca package, Journal of Statistical Software, 20(3).
Van Buuren, S., & Heiser, W. J. (1989). Clustering n objects in k groups under optimal scaling of variables. Psychometrika, 54, 699–706.
Vichi, M., & Kiers, H. (2001). Factorial k-means analysis for two way data. Computational Statistics & Data Analysis, 37, 49–64.
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Iodice D’Enza, A., Van de Velden, M., Palumbo, F. (2014). On Joint Dimension Reduction and Clustering of Categorical Data. In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_18
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DOI: https://doi.org/10.1007/978-3-319-06692-9_18
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