Publication details

Journal Article

Minimum Information Loss Cluster Analysis for Cathegorical Data

Grim Jiří, Hora Jan

: Lecture Notes in Computer Science vol.2007, p. 233-247

: International Conference on Machine Learning and Data Mining MLDM 2007 /5./, (Leipzig, DE, 18.07.2007-20.07.2007)

: CEZ:AV0Z10750506

: 1M0572, GA MŠk, GA102/07/1594, GA ČR, 2C06019, GA MŠk

: Cluster Analysis, Cathegorical Data, EM algorithm

(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.

(cze): Shluková analýza kategoriálních dat s využitím kriteria minimální ztráty informace.

: BD