"Grafick\u00E9 metody v EA, CA a PCA pro anal\u00FDzu v\u00EDcerozm\u011Brn\u00FDch dat"@cs . "[49A6442B6C01]" . . "1"^^ . . "V" . "7"^^ . . . "2006-12-11+01:00"^^ . "Grafick\u00E9 metody v EA, CA a PCA pro anal\u00FDzu v\u00EDcerozm\u011Brn\u00FDch dat" . "1"^^ . . "Olomouc" . "RIV/00216275:25210/06:00009062!RIV10-MSM-25210___" . "Olomouc" . "Votobia" . . "Graphical Methods in EA, CA AND PCA for Multidimensional Data Analysis"@en . "Multidimensional data are usually represented by the matrix with n objects in rows and m attributes in columns. The aim of the analysis is to find the hidden relations and structures in the objects and attributes. Diagrams of the exploratory data analysis (EA) allow quick understanding and evaluating of similarity or exceptionality of the objects or understanding of relationships among the attributes. Some methods of multidimensional data analysis (e.g. cluster analysis (CA) or principal component analysis (PCA)) allows to set objects or attributes into clusters or find the latent attributes ? in this case is also possible to use the graphical methods. The aim of this paper is to show the usage of graphical methods in exploratory and multidimensional data analysis."@en . "Aktu\u00E1ln\u00ED probl\u00E9my pedagogiky ve v\u00FDzkumech student\u016F doktorsk\u00FDch studijn\u00EDch program\u016F IV. Sborn\u00EDk p\u0159\u00EDsp\u011Bvk\u016F z IV. ro\u010Dn\u00EDku studentsk\u00E9 v\u011Bdeck\u00E9 konference" . "RIV/00216275:25210/06:00009062" . . "Graphical methods; exploratory analysis; cluster analysis; principal component analysis"@en . "25210" . "Graphical Methods in EA, CA AND PCA for Multidimensional Data Analysis"@en . . "80-7220-280-4" . . "Grafick\u00E9 metody v EA, CA a PCA pro anal\u00FDzu v\u00EDcerozm\u011Brn\u00FDch dat" . . "Myslivec, Jaroslav" . . "V\u00EDcerozm\u011Brn\u00E1 data jsou v\u011Bt\u0161inou reprezentovan\u00E1 matic\u00ED s n objekty v \u0159\u00E1dc\u00EDch a m atributy ve sloupc\u00EDch. C\u00EDlem anal\u00FDzy je \u010Dasto nalezen\u00ED skryt\u00FDch vazeb a struktur jak v objektech tak i v atributech. Grafy exploratorn\u00ED anal\u00FDzy dat (EA) umo\u017E\u0148uj\u00ED rychl\u00E9 pochopen\u00ED a posouzen\u00ED podobnosti \u010Di v\u00FDjime\u010Dnosti jednotliv\u00FDch objekt\u016F, stejn\u011B tak i pochopen\u00ED vztah\u016F mezi atributy. N\u011Bkter\u00E9 metody v\u00EDcerozm\u011Brn\u00E9 anal\u00FDzy (nap\u0159. shlukov\u00E1 anal\u00FDza (CA) nebo anal\u00FDza hlavn\u00EDch komponent (PCA)) umo\u017E\u0148uj\u00ED seskupit objekty i atributy do shluk\u016F nebo naj\u00EDt latentn\u00ED atributy ? i zde lze s \u00FAsp\u011Bchem pou\u017E\u00EDt grafick\u00E9 zobrazen\u00ED. C\u00EDlem t\u00E9to pr\u00E1ce je uk\u00E1zat vyu\u017Eit\u00ED grafick\u00FDch metod v exploratorn\u00ED i v\u00EDcerozm\u011Brn\u00E9 anal\u00FDze dat."@cs . . "477257" . . . "Grafick\u00E9 metody v EA, CA a PCA pro anal\u00FDzu v\u00EDcerozm\u011Brn\u00FDch dat"@cs . . "V\u00EDcerozm\u011Brn\u00E1 data jsou v\u011Bt\u0161inou reprezentovan\u00E1 matic\u00ED s n objekty v \u0159\u00E1dc\u00EDch a m atributy ve sloupc\u00EDch. C\u00EDlem anal\u00FDzy je \u010Dasto nalezen\u00ED skryt\u00FDch vazeb a struktur jak v objektech tak i v atributech. Grafy exploratorn\u00ED anal\u00FDzy dat (EA) umo\u017E\u0148uj\u00ED rychl\u00E9 pochopen\u00ED a posouzen\u00ED podobnosti \u010Di v\u00FDjime\u010Dnosti jednotliv\u00FDch objekt\u016F, stejn\u011B tak i pochopen\u00ED vztah\u016F mezi atributy. N\u011Bkter\u00E9 metody v\u00EDcerozm\u011Brn\u00E9 anal\u00FDzy (nap\u0159. shlukov\u00E1 anal\u00FDza (CA) nebo anal\u00FDza hlavn\u00EDch komponent (PCA)) umo\u017E\u0148uj\u00ED seskupit objekty i atributy do shluk\u016F nebo naj\u00EDt latentn\u00ED atributy ? i zde lze s \u00FAsp\u011Bchem pou\u017E\u00EDt grafick\u00E9 zobrazen\u00ED. C\u00EDlem t\u00E9to pr\u00E1ce je uk\u00E1zat vyu\u017Eit\u00ED grafick\u00FDch metod v exploratorn\u00ED i v\u00EDcerozm\u011Brn\u00E9 anal\u00FDze dat." . .