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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A03149558%21RIV09-MSM-21230___
rdf:type
skos:Concept n12:Vysledek
dcterms:description
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. 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.
dcterms:title
A Comparison of Hierarchical Clustering Methods and Self-Organizing Map A Comparison of Hierarchical Clustering Methods and Self-Organizing Map Porovnani hierarchickych metod shlukovani a samoorganizujici se mapy
skos:prefLabel
A Comparison of Hierarchical Clustering Methods and Self-Organizing Map Porovnani hierarchickych metod shlukovani a samoorganizujici se mapy A Comparison of Hierarchical Clustering Methods and Self-Organizing Map
skos:notation
RIV/68407700:21230/08:03149558!RIV09-MSM-21230___
n4:aktivita
n20:S
n4:aktivity
S
n4:dodaniDat
n17:2009
n4:domaciTvurceVysledku
n7:3534553
n4:druhVysledku
n14:D
n4:duvernostUdaju
n5:S
n4:entitaPredkladatele
n16:predkladatel
n4:idSjednocenehoVysledku
354102
n4:idVysledku
RIV/68407700:21230/08:03149558
n4:jazykVysledku
n10:eng
n4:klicovaSlova
Hierarchical; SOM; Two-Stage Density Linkage
n4:klicoveSlovo
n8:Two-Stage%20Density%20Linkage n8:SOM n8:Hierarchical
n4:kontrolniKodProRIV
[3D61683D136C]
n4:mistoKonaniAkce
Žilina
n4:mistoVydani
Žilina
n4:nazevZdroje
Digital Technologies 2008
n4:obor
n19:JA
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:rokUplatneniVysledku
n17:2008
n4:tvurceVysledku
Zetocha, Petr
n4:typAkce
n11:WRD
n4:zahajeniAkce
2008-11-20+01:00
s:numberOfPages
5
n13:hasPublisher
Žilinská univerzita v Žiline. Elektrotechnická fakulta
n15:isbn
978-80-8070-953-2
n18:organizacniJednotka
21230