In this paper we face the problem of clustering mixedmode data by assuming that the observed binary variables aregenerated from latent continuous variables. We perform a principalcomponents analysis on the matrix of tetrachoric correlations andwe estimate the scores of each latent variable and reach a datamatrix with continuous variables to be used in fully Guassianmodels or in the k-means cluster analysis. Results on a simulationstudy and on a real data set are reported
Mixed mode data clustering: an approach based on tetrachoric correlations / Morlini, Isabella. - STAMPA. - 1:(2008), pp. 73-76. (Intervento presentato al convegno First Joint Meeting of the Sfc and the CLADAG of the Italian Statistical Society tenutosi a Caserta, Italy nel 11-13 Giugno 2008).
Mixed mode data clustering: an approach based on tetrachoric correlations
MORLINI, Isabella
2008
Abstract
In this paper we face the problem of clustering mixedmode data by assuming that the observed binary variables aregenerated from latent continuous variables. We perform a principalcomponents analysis on the matrix of tetrachoric correlations andwe estimate the scores of each latent variable and reach a datamatrix with continuous variables to be used in fully Guassianmodels or in the k-means cluster analysis. Results on a simulationstudy and on a real data set are reported
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