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  • Příspěvek se zabývá shlukováním řečových signálů dětských promluv. Hlavním problémem hierarchického shlukování je velká tendence k chybovosti, pokud se shlukuje na datech, které neobsahují ideální shluky. Použito bylo 4 datových množin s rozdílnou strukturou. Testovalo se 8 hierarchických metos a samoorganizujici se mapa. Pro všechny datové množiny měla SOM nejvyšší počet správně klasifikovaných vektorů. (cs)
  • In this paper, we use clustering analysis for separating phonemes from children's speech. A major problem with hierarchical clustering methods is the tendency for a misclassification when the real data departs from the ideal conditions of compact isolated clusters. A phoneme data set has structural imperfections that confound the identification of clusters. In this paper we demonstrate that the Self-Organizing Map (SOM) is superior to the hierarchical clustering methods. The performance of the SOM and eight hierarchical clustering methods is tested on 4 data sets with various levels of imperfections that include data dispersion and nonuniform cluster densities. The higher accuracy and robustness of the SOM can improve the effectiveness of decisions and research based on clustering speech databases.
  • In this paper, we use clustering analysis for separating phonemes from children's speech. A major problem with hierarchical clustering methods is the tendency for a misclassification when the real data departs from the ideal conditions of compact isolated clusters. A phoneme data set has structural imperfections that confound the identification of clusters. In this paper we demonstrate that the Self-Organizing Map (SOM) is superior to the hierarchical clustering methods. The performance of the SOM and eight hierarchical clustering methods is tested on 4 data sets with various levels of imperfections that include data dispersion and nonuniform cluster densities. The higher accuracy and robustness of the SOM can improve the effectiveness of decisions and research based on clustering speech databases. (en)
Title
  • A Comparison of Hierarchical Clustering Methods and Self-Organizing Map
  • A Comparison of Hierarchical Clustering Methods and Self-Organizing Map (en)
  • Porovnani hierarchickych metod shlukovani a samoorganizujici se mapy (cs)
skos:prefLabel
  • A Comparison of Hierarchical Clustering Methods and Self-Organizing Map
  • A Comparison of Hierarchical Clustering Methods and Self-Organizing Map (en)
  • Porovnani hierarchickych metod shlukovani a samoorganizujici se mapy (cs)
skos:notation
  • RIV/68407700:21230/08:03149558!RIV09-MSM-21230___
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
  • 354102
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/08:03149558
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Hierarchical; SOM; Two-Stage Density Linkage (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [3D61683D136C]
http://linked.open...v/mistoKonaniAkce
  • Žilina
http://linked.open...i/riv/mistoVydani
  • Žilina
http://linked.open...i/riv/nazevZdroje
  • Digital Technologies 2008
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Zetocha, Petr
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Žilinská univerzita v Žiline. Elektrotechnická fakulta
https://schema.org/isbn
  • 978-80-8070-953-2
http://localhost/t...ganizacniJednotka
  • 21230
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