Minimum Information Loss Cluster Analysis for Cathegorical Data

0086490 - ÚTIA 2008 RIV DE eng J - Journal Article
Grim, Jiří - Hora, Jan
Minimum Information Loss Cluster Analysis for Cathegorical Data.
[Shluková analýza kategoriálních dat s minimální ztrátou informace.]
Lecture Notes in Computer Science. Roč. 2007, Č. 4571 (2007), s. 233-247. ISSN 0302-9743.
[International Conference on Machine Learning and Data Mining MLDM 2007 /5./. Leipzig, 18.07.2007-20.07.2007]
R&D Projects: GA MŠMT 1M0572; GA ČR GA102/07/1594
Grant - others:GA MŠk(CZ) 2C06019
Institutional research plan: CEZ:AV0Z10750506
Keywords : Cluster Analysis * Cathegorical Data * EM algorithm
Subject RIV: BD - Theory of Information
Impact factor: 0.402, year: 2005

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.
Permanent Link: http://hdl.handle.net/11104/0148741