2011 | OriginalPaper | Chapter
Mixed Mode Data Clustering: An Approach Based on Tetrachoric Correlations
Author : Isabella Morlini
Published in: Classification and Multivariate Analysis for Complex Data Structures
Publisher: Springer Berlin Heidelberg
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In this paper we face the problem of clustering mixed mode data by assuming that the observed binary variables are generated from latent continuous variables. We perform a principal components analysis on the matrix of tetrachoric correlations and we then estimate the scores of each latent variable and construct a data matrix with continuous variables to be used in fully Guassian mixture models or in the k-means cluster analysis. The calculation of the expected a posteriori (EAP) estimates may proceed by simply considering a limited number of quadrature points. Main results on a simulation study and on a real data set are reported.