About: Clustering via the Distribution Mixtures     Goto   Sponge   Distinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • The finite distribution mixtures present a wide class of probability distributions. Apart from the obvious applications, the mixtures are successfully applied in the model based clustering. If we constraint the members of the mixture to arise from one specific family or type of parametric distributions, each cluster would refer to one component of the mixture. The membership of the observed sample to a cluster is given simply as the maximum probability on the components of the mixture, i.e. by the Mahalanobis distance, and weighted by the weights of the mixture. This approach is feasible even for overlapping clusters and strongly uneven numbers of the members of the clusters, where standard methods of cluster analysis fall short. We focus on the problem of fitting the mixture to observed sample using the maximum likelihood approach and the EM algorithm, as well as the assessment of the optimal number of components.
  • The finite distribution mixtures present a wide class of probability distributions. Apart from the obvious applications, the mixtures are successfully applied in the model based clustering. If we constraint the members of the mixture to arise from one specific family or type of parametric distributions, each cluster would refer to one component of the mixture. The membership of the observed sample to a cluster is given simply as the maximum probability on the components of the mixture, i.e. by the Mahalanobis distance, and weighted by the weights of the mixture. This approach is feasible even for overlapping clusters and strongly uneven numbers of the members of the clusters, where standard methods of cluster analysis fall short. We focus on the problem of fitting the mixture to observed sample using the maximum likelihood approach and the EM algorithm, as well as the assessment of the optimal number of components. (en)
Title
  • Clustering via the Distribution Mixtures
  • Clustering via the Distribution Mixtures (en)
skos:prefLabel
  • Clustering via the Distribution Mixtures
  • Clustering via the Distribution Mixtures (en)
skos:notation
  • RIV/68407700:21340/10:00176166!RIV11-MSM-21340___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
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
  • 251008
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21340/10:00176166
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • EM algorithm; Parameter estimation; Iterative numerical procedure; Informational criteria (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [C9EF91E944D4]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Praha
http://linked.open...i/riv/nazevZdroje
  • Doktorandské dny 2010
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Tláskal, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Česká technika - nakladatelství ČVUT
https://schema.org/isbn
  • 978-80-01-04644-9
http://localhost/t...ganizacniJednotka
  • 21340
is http://linked.open...avai/riv/vysledek of
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 112 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software