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Data Mining Module of a Data Mining System on NetBeans Platform
Výtvar, Jaromír ; Křivka, Zbyněk (referee) ; Zendulka, Jaroslav (advisor)
The aim of this work is to get basic overview about the process of obtaining knowledge from databases - datamining and to analyze the datamining system developed at FIT BUT on the NetBeans platform in order to create a new mining module. We decided to implement a module for mining outliers and to extend existing regression module with multiple linear regression using generalized linear models. New methods using existing methods of Oracle Data Mining.
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Statistical analysis of big industrial data
Zamazal, Petr ; Popela, Pavel (referee) ; Šomplák, Radovan (advisor)
This thesis deals with processing of real data regarding waste collection. It describes select parts of the fields of statistical tests, identification of outliers, correlation analysis and linear regression. This theoretical basis is applied through the programming language Python to process the data into a form suitable for creating linear models. Final models explain between 70 \% and 85 \% variability. Finally, the information obtained through this analysis is used to specify recommendations for the waste management company.
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The Introduction and Application of General Regression Model
Hrabec, Pavel ; Štarha, Pavel (referee) ; Bednář, Josef (advisor)
This thesis sumarizes in detail general linear regression model, including testing statistics for coefficients, submodels, predictions and mostly tests of outliers and large leverage points. It describes how to include categorial variables into regression model. This model was applied to describe saturation of photographs of bread, where input variables were, type of flour, type of addition and concntration of flour. After identification of outliers it was possible to create mathematical model with high coefficient of determination, which will be usefull for experts in food industry for preliminar identification of possible composition of bread.
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Data mining analysis of chemical bonds in alloys
Nechutová, Vendula ; Šeda, Miloš (referee) ; Roupec, Jan (advisor)
The thesis deals with aplication of data mining methods for the analysis of two Ni3Si supercells, one with a stable grain boundary and the second one with unstable grain boundary. DOS and COHP curves are examined using selected curve matching methods. The surroundings of the individual atoms are examined by the Voronoi diagram. This information was used to reveal the differences in binding between stable and unstable supercell.
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Statistical analysis of big industrial data
Zamazal, Petr ; Popela, Pavel (referee) ; Šomplák, Radovan (advisor)
This thesis deals with processing of real data regarding waste collection. It describes select parts of the fields of statistical tests, identification of outliers, correlation analysis and linear regression. This theoretical basis is applied through the programming language Python to process the data into a form suitable for creating linear models. Final models explain between 70 \% and 85 \% variability. Finally, the information obtained through this analysis is used to specify recommendations for the waste management company.
|
|
|
Data mining analysis of chemical bonds in alloys
Nechutová, Vendula ; Šeda, Miloš (referee) ; Roupec, Jan (advisor)
The thesis deals with aplication of data mining methods for the analysis of two Ni3Si supercells, one with a stable grain boundary and the second one with unstable grain boundary. DOS and COHP curves are examined using selected curve matching methods. The surroundings of the individual atoms are examined by the Voronoi diagram. This information was used to reveal the differences in binding between stable and unstable supercell.
|
|
|
Data Mining Module of a Data Mining System on NetBeans Platform
Výtvar, Jaromír ; Křivka, Zbyněk (referee) ; Zendulka, Jaroslav (advisor)
The aim of this work is to get basic overview about the process of obtaining knowledge from databases - datamining and to analyze the datamining system developed at FIT BUT on the NetBeans platform in order to create a new mining module. We decided to implement a module for mining outliers and to extend existing regression module with multiple linear regression using generalized linear models. New methods using existing methods of Oracle Data Mining.
|
|
|
The Introduction and Application of General Regression Model
Hrabec, Pavel ; Štarha, Pavel (referee) ; Bednář, Josef (advisor)
This thesis sumarizes in detail general linear regression model, including testing statistics for coefficients, submodels, predictions and mostly tests of outliers and large leverage points. It describes how to include categorial variables into regression model. This model was applied to describe saturation of photographs of bread, where input variables were, type of flour, type of addition and concntration of flour. After identification of outliers it was possible to create mathematical model with high coefficient of determination, which will be usefull for experts in food industry for preliminar identification of possible composition of bread.
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