This HTML5 document contains 44 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n8http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F61989100%3A27360%2F05%3A00012968%21RIV06-MSM-27360___/
n17http://localhost/temp/predkladatel/
n12http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n16http://linked.opendata.cz/ontology/domain/vavai/
n18http://linked.opendata.cz/resource/domain/vavai/zamer/
shttp://schema.org/
n4http://linked.opendata.cz/ontology/domain/vavai/riv/
skoshttp://www.w3.org/2004/02/skos/core#
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n14http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n10http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n15http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n6http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n11http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n9http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F61989100%3A27360%2F05%3A00012968%21RIV06-MSM-27360___
rdf:type
skos:Concept n16:Vysledek
dcterms:description
Principal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2 % of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data Principal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2 % of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data Vzorky alterovaných uhlí byly hodnoceny metodou hlavních komponent s použitím algoritmu SVD. Pomocí nalezených hlavních komponent byly vzorky rozděleny do skupin podle jejich geochemického složení. Výsledky tohoto dělení byly porovnány s výsledky hierarchické klastrovací analýzy. Interpretace nalezených hlavních komponent byla provedena pomocí faktorové analýzy.
dcterms:title
Analýza hlavních komponent geochemický dat založená na SVD SVD-Based Principal Component Analysis of Geochemical Data SVD-Based Principal Component Analysis of Geochemical Data
skos:prefLabel
SVD-Based Principal Component Analysis of Geochemical Data SVD-Based Principal Component Analysis of Geochemical Data Analýza hlavních komponent geochemický dat založená na SVD
skos:notation
RIV/61989100:27360/05:00012968!RIV06-MSM-27360___
n4:strany
731-741
n4:aktivita
n6:Z
n4:aktivity
Z(MSM6198910016)
n4:cisloPeriodika
3
n4:dodaniDat
n9:2006
n4:domaciTvurceVysledku
n12:8145652
n4:druhVysledku
n13:J
n4:duvernostUdaju
n10:S
n4:entitaPredkladatele
n8:predkladatel
n4:idSjednocenehoVysledku
545561
n4:idVysledku
RIV/61989100:27360/05:00012968
n4:jazykVysledku
n15:eng
n4:klicovaSlova
Analýza hlavních komponent; rozklad singulárních čísel; faktorová analýza; hierarchická klastrová analýza; alterovaná uhlí; geochemie
n4:klicoveSlovo
n14:rozklad%20singul%C3%A1rn%C3%ADch%20%C4%8D%C3%ADsel n14:faktorov%C3%A1%20anal%C3%BDza n14:Anal%C3%BDza%20hlavn%C3%ADch%20komponent n14:geochemie n14:alterovan%C3%A1%20uhl%C3%AD n14:hierarchick%C3%A1%20klastrov%C3%A1%20anal%C3%BDza
n4:kodStatuVydavatele
PL - Polská republika
n4:kontrolniKodProRIV
[F3E1145E31A7]
n4:nazevZdroje
Central European Journal of Chemistry
n4:obor
n11:CB
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:rokUplatneniVysledku
n9:2005
n4:svazekPeriodika
4
n4:tvurceVysledku
Praus, Petr
n4:zamer
n18:MSM6198910016
s:issn
0364-5916
s:numberOfPages
11
n17:organizacniJednotka
27360