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Knowledge Discovery from Databases with Use of the R Language
Krutý, Peter ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This thesis is focused on the field of knowledge discovery from databases. Main objective is to research possibilities of R language and its support in this area. Support is researched by experiments using appropriate data sets. More detailed attention is given to the methods of classification, clustering and association rules learning. The output of the thesis is comparison of methods application in R and defining the suitability of using language for knowledge discovery from databases.
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Knowledge Discovery in Multimedia Databases
Jirmásek, Tomáš ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This master's thesis deals with knowledge discovery in databases, especially basic methods of classification and prediction used for data mining are described here. The next chapter contains introduction to multimedia databases and knowledge discovery in multimedia databases. The main goal of this chapter was to focus on extraction of low level features from video data and images. In the next parts of this work, there is described data set and results of experiments in applications RapidMiner, LibSVM and own developed application. The last chapter summarises results of used methods for high level feature extraction from low level description of data.
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Knowledge Discovery from Databases with Use of the R Language
Krutý, Peter ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This thesis is focused on the field of knowledge discovery from databases. Main objective is to research possibilities of R language and its support in this area. Support is researched by experiments using appropriate data sets. More detailed attention is given to the methods of classification, clustering and association rules learning. The output of the thesis is comparison of methods application in R and defining the suitability of using language for knowledge discovery from databases.
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Knowledge Discovery in Multimedia Databases
Jirmásek, Tomáš ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This master's thesis deals with knowledge discovery in databases, especially basic methods of classification and prediction used for data mining are described here. The next chapter contains introduction to multimedia databases and knowledge discovery in multimedia databases. The main goal of this chapter was to focus on extraction of low level features from video data and images. In the next parts of this work, there is described data set and results of experiments in applications RapidMiner, LibSVM and own developed application. The last chapter summarises results of used methods for high level feature extraction from low level description of data.
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Design and implementation of Data Mining model with MS SQL Server technology
Peroutka, Lukáš ; Maryška, Miloš (advisor) ; Smutný, Zdeněk (referee)
This thesis focuses on design and implementation of a data mining solution with real-world data. The task is analysed, processed and its results evaluated. The mined data set contains study records of students from University of Economics, Prague (VŠE) over the course of past three years. First part of the thesis focuses on theory of data mining, definition of the term, history and development of this particular field. Current best practices and meth-odology are described, as well as methods for determining the quality of data and methods for data pre-processing ahead of the actual data mining task. The most common data mining techniques are introduced, including their basic concepts, advantages and disadvantages. The theoretical basis is then used to implement a concrete data mining solution with educational data. The source data set is described, analysed and some of the data are chosen as input for created models. The solution is based on MS SQL Server data mining platform and it's goal is to find, describe and analyse potential as-sociations and dependencies in data. Results of respective models are evaluated, including their potential added value. Also mentioned are possible extensions and suggestions for further development of the solution.
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Corporate Bankruptcy Prediction Using Bayesian Classifiers
Hátle, Lukáš ; Witzany, Jiří (advisor) ; Málek, Jiří (referee)
The aim of this study is to evaluate feasibility of using Bayes classifiers for predicting corporate bankruptcies. The results obtain show that Bayes classifiers do reach comparable results to then more commonly used methods such the logistic regression and the decision trees. The comparison has been carried out based on Czech and Polish data sets. The overall accuracy rate of these so called naive Bayes classifiers, using entropic discretization along with the hybrid pre-selection of the explanatory attributes, reaches 77.19 % for the Czech dataset and 79.76 % for the Polish set respectively. The AUC values for these data sets are 0.81 and 0.87. The results obtained for the Polish data set have been compared to the already published articles by Tsai (2009) and Wang et al. (2014) who applied different classification algorithms. The method proposed in my study, when compared to the above earlier works, comes out as quite successful. The thesis also includes comparing various approaches as regards the discretisation of numerical attributes and selecting the relevant explanatory attributes. These are the key issues for increasing performance of the naive Bayes classifiers
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