0086490 20240103184506.820071002235959.9 15 s. cav_un_epca*02585180302-97432007233-247 Cluster Analysis Cathegorical Data EM algorithm cav_un_auth*0101091 Grim Jiří UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0230019 Hora Jan UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. 1M0572 GA MŠk cav_un_auth*0001814 GA102/07/1594 GA ČR cav_un_auth*0228611 2C06019 GA MŠk CZ cav_un_auth*0216518 CEZ:AV0Z10750506 The EM algorithm has been used repeatedly to identify latent classes in categorical data by estimating finite distribution mixtures of produkt components. Unfortunately, the underlying mixtures are not uniquely identifiable and, moreover, the estimated mixture parameters are starting-point dependent. For this reason we use the latent class model only to define a set of ``elementary'' classes by estimating a mixture of a large number components. We propose a hierarchical ``bottom up'' cluster analysis based on unifying the elementary latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion. Shluková analýza kategoriálních dat s využitím kriteria minimální ztráty informace. cav_un_auth*0230771 International Conference on Machine Learning and Data Mining MLDM 2007 /5./ Leipzig 18.07.2007-20.07.2007 DE 2008 BD http://hdl.handle.net/11104/0148741 2007 cav_un_epca*0258518 Lecture Notes in Computer Science 0302-9743 Roč. 2007 Č. 4571 2007 233 247