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Description
  • Shluková analýza kategoriálních dat s využitím kriteria minimální ztráty informace. (cs)
  • 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.
  • 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. (en)
Title
  • Minimum Information Loss Cluster Analysis for Cathegorical Data
  • Shluková analýza kategoriálních dat s minimální ztrátou informace (cs)
  • Minimum Information Loss Cluster Analysis for Cathegorical Data (en)
skos:prefLabel
  • Minimum Information Loss Cluster Analysis for Cathegorical Data
  • Shluková analýza kategoriálních dat s minimální ztrátou informace (cs)
  • Minimum Information Loss Cluster Analysis for Cathegorical Data (en)
skos:notation
  • RIV/67985556:_____/07:00086490!RIV08-AV0-67985556
http://linked.open.../vavai/riv/strany
  • 233;247
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0572), P(GA102/07/1594), Z(AV0Z10750506)
http://linked.open...iv/cisloPeriodika
  • 4571
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 433991
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/07:00086490
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Cluster Analysis; Cathegorical Data; EM algorithm (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • DE - Spolková republika Německo
http://linked.open...ontrolniKodProRIV
  • [0087F67D8121]
http://linked.open...i/riv/nazevZdroje
  • Lecture Notes in Computer Science
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 2007
http://linked.open...iv/tvurceVysledku
  • Hora, Jan
  • Grim, Jiří
http://linked.open...n/vavai/riv/zamer
issn
  • 0302-9743
number of pages
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