Doktorandské dny 2007. - Praha : Česká technika ČVUT, 2007
- ISBN 978-80-01-03913-7
S. 57-66
Number of pages
10 s.
Action
Doktorandské dny 2007
Event date
16.11.2007
VEvent location
Praha
Country
CZ - Czech Republic
Event type
CST
Language
eng - English
Country
CZ - Czech Republic
Keywords
EM algorithm ; distribution mixtures ; cluster analysis ; cathegorial data
Subject RIV
BB - Applied Statistics, Operational Research
R&D Projects
GA102/07/1594 GA ČR - Czech Science Foundation (CSF)
1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
CEZ
AV0Z10750506 - UTIA-B (2005-2011)
Annotation
The EM algorithm has been used repeatedly to identify latent classes in categorical data by estimating finite distribution mixtures of product 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. As such a mixture we use also an optimally smoothed kernel estimate. 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.
