bibtype	J - Journal Article
ARLID	0086490
utime	20240103184506.8
mtime	20071002235959.9
title (primary) (eng)	Minimum Information Loss Cluster Analysis for Cathegorical Data
specification	page_count 15 s.
serial	ARLID cav_un_epca*0258518 ISSN 0302-9743 title Lecture Notes in Computer Science volume_id 2007 page_num 233-247
title (cze)	Shluková analýza kategoriálních dat s minimální ztrátou informace
keyword	Cluster Analysis
keyword	Cathegorical Data
keyword	EM algorithm
author (primary)	ARLID cav_un_auth*0101091 name1 Grim name2 Jiří institution UTIA-B full_dept Department of Pattern Recognition fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0230019 name1 Hora name2 Jan institution UTIA-B fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
cas_special	project project_id 1M0572 agency GA MŠk ARLID cav_un_auth*0001814 project project_id GA102/07/1594 agency GA ČR ARLID cav_un_auth*0228611 project project_id 2C06019 agency GA MŠk country CZ ARLID cav_un_auth*0216518 research CEZ:AV0Z10750506 abstract (eng) 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. abstract (cze) Shluková analýza kategoriálních dat s využitím kriteria minimální ztráty informace. action ARLID cav_un_auth*0230771 name International Conference on Machine Learning and Data Mining MLDM 2007 /5./ place Leipzig dates 18.07.2007-20.07.2007 country DE reportyear 2008 RIV BD permalink http://hdl.handle.net/11104/0148741 arlyear 2007 mrcbU63 cav_un_epca*0258518 Lecture Notes in Computer Science 0302-9743 Roč. 2007 Č. 4571 2007 233 247