Mixed mode data clustering: An approach based on tetrachoric correlations

Abstract

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. © Springer-Verlag Berlin Heidelberg 2011.