"SVD-Based Principal Component Analysis of Geochemical Data" . "731-741" . "1"^^ . "11"^^ . "Z(MSM6198910016)" . "RIV/61989100:27360/05:00012968" . "1"^^ . . "4" . "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"@en . "545561" . . . "0364-5916" . . "Praus, Petr" . "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" . . "RIV/61989100:27360/05:00012968!RIV06-MSM-27360___" . . "[F3E1145E31A7]" . . . "Anal\u00FDza hlavn\u00EDch komponent geochemick\u00FD dat zalo\u017Een\u00E1 na SVD"@cs . "SVD-Based Principal Component Analysis of Geochemical Data"@en . "Vzorky alterovan\u00FDch uhl\u00ED byly hodnoceny metodou hlavn\u00EDch komponent s pou\u017Eit\u00EDm algoritmu SVD. Pomoc\u00ED nalezen\u00FDch hlavn\u00EDch komponent byly vzorky rozd\u011Bleny do skupin podle jejich geochemick\u00E9ho slo\u017Een\u00ED. V\u00FDsledky tohoto d\u011Blen\u00ED byly porovn\u00E1ny s v\u00FDsledky hierarchick\u00E9 klastrovac\u00ED anal\u00FDzy. Interpretace nalezen\u00FDch hlavn\u00EDch komponent byla provedena pomoc\u00ED faktorov\u00E9 anal\u00FDzy."@cs . . . "Central European Journal of Chemistry" . . . . "3" . . "PL - Polsk\u00E1 republika" . . . "SVD-Based Principal Component Analysis of Geochemical Data"@en . "Anal\u00FDza hlavn\u00EDch komponent geochemick\u00FD dat zalo\u017Een\u00E1 na SVD"@cs . . "27360" . "SVD-Based Principal Component Analysis of Geochemical Data" . "Anal\u00FDza hlavn\u00EDch komponent; rozklad singul\u00E1rn\u00EDch \u010D\u00EDsel; faktorov\u00E1 anal\u00FDza; hierarchick\u00E1 klastrov\u00E1 anal\u00FDza; alterovan\u00E1 uhl\u00ED; geochemie"@en . .