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  • 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 assume the latent class model only to define a set of elementary properties and the related statistical decision problem. In order to avoid the problem of unique identification of latent classes we propose a hierarchical ``bottom up'' cluster analysis based on unifying the 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 product components. Unfortunately, the underlying mixtures are not uniquely identifiable and moreover, the estimated mixture parameters are starting-point dependent. For this reason we assume the latent class model only to define a set of elementary properties and the related statistical decision problem. In order to avoid the problem of unique identification of latent classes we propose a hierarchical ``bottom up'' cluster analysis based on unifying the latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion. (en)
Title
  • Minimum Information Loss Cluster Analysis for Categorical Data
  • Minimum Information Loss Cluster Analysis for Categorical Data (en)
skos:prefLabel
  • Minimum Information Loss Cluster Analysis for Categorical Data
  • Minimum Information Loss Cluster Analysis for Categorical Data (en)
skos:notation
  • RIV/68407700:21340/06:00125818!RIV11-MSM-21340___
http://linked.open...avai/riv/aktivita
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  • V
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
  • 485911
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21340/06:00125818
http://linked.open...riv/jazykVysledku
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  • cluster analysis; categorical data; EM algorithm (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [95DC161ED977]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Praha
http://linked.open...i/riv/nazevZdroje
  • Doktorandské dny 2006
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Hora, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • Česká technika - nakladatelství ČVUT
https://schema.org/isbn
  • 80-01-03554-9
http://localhost/t...ganizacniJednotka
  • 21340
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