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Methods for Classification of WWW Pages
Svoboda, Pavel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The main goal of this master's thesis was to study the main principles of classification methods. Basic principles of knowledge discovery process, data mining and using an external class CSSBox are described. Special attantion was paid to implementation of a ,,k-nearest neighbors`` classification method. The first objective of this work was to create training and testing data described by 'n' attributes. The second objective was to perform experimental analysis to determine a good value for 'k', the number of neighbors.
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Knowledge Data Discovery
Jirmásek, Tomáš ; Chmelař, Petr (referee) ; Jurka, Pavel (advisor)
This bachelor's thesis deals with knowledge discovery in databases and is focused on Bayesian classification. The main goal of this thesis was to implement one of the methods of data mining and to verify its functionality on chosen data set. The application is implemented in programming language Java. MySQL database was chosen as a data storage for data set prepared to extract patterns from it. Information needed to start data mining task are gained from input DMSL document. The results of data mining are also stored into output DMSL document. The DMSL language had to be extended because of implemented method, Bayesian classification.
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Knowledge Discovery in MS SQL Environment
Pijáček, Roman ; Šebek, Michal (referee) ; Bartík, Vladimír (advisor)
This Bachelor's thesis deals with issue of knowledge discovery in databases in MS SQL Server 2008. After the initial entry into the field of knowledge discovery in databases, general principles and algorithms of used data mining methods (Bayesian classification, association rules, decision trees, cluster analysis) are explained in detail. In the practical part of this thesis there is designed, implemented and tested desktop application which allows the user to discover knowledge and hidden information from database. The application uses methods built into the SQL Server 2008 and the Apriori method for mining strong association rules. In the end, there are discussed further possible expansion of the existing project and an evaluation of the results.
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Methods for Classification of WWW Pages
Svoboda, Pavel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The main goal of this master's thesis was to study the main principles of classification methods. Basic principles of knowledge discovery process, data mining and using an external class CSSBox are described. Special attantion was paid to implementation of a ,,k-nearest neighbors`` classification method. The first objective of this work was to create training and testing data described by 'n' attributes. The second objective was to perform experimental analysis to determine a good value for 'k', the number of neighbors.
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Bayesian classification of digital images by web application
Talich, M. ; Böhm, O. ; Soukup, Lubomír
The contribution introduces web application for image classification that has been developed at the Research Institute of Geodesy, Topography and Cartography in the framework of grant project InGeoCalc (supported by Ministry of education of the Czech Republic). The web application is aimed to display, examine and classify digital image data. The data are expected to be obtained from Internet by means of Web Map Services (WMS) or from other sources (possibly non-registered). Image data from different sources can be combined and presented as composition of layers (coverage) with adjustable degrees of transparency. After gathering the data, Bayesian (supervised) classification is applied to distinguish separate regions in the image. User can choose between several classification methods and adjust pertinent parameters. Furthermore, several subsequent basic analytical tools are offered, namely computation of distances, areas or perimeters related to the classified regions, simple statistical summaries about classification results (e.g. distribution of classes, percentage of non-classified regions, etc.). The classification results and registration parameters can be saved for further use. The web application is based on common Internet standards (HTML, Javascript, SVG). The only requirement for running the application is an up-to-date Internet browser supporting SVG (Scalable Vector Graphics). Typical usage of the web application can involve land cover mapping based on satellite or aerial images. The application is available free of charge for any Internet user.
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Practical applications of data mining technologies in health insurance companies
Kulhavý, Lukáš ; Pour, Jan (advisor) ; Kučera, Petr (referee)
This thesis focuses on data mining technology and its possible practical use in the field of health insurance companies. Thesis defines the term data mining and its relation to the term knowledge discovery in databases. The term data mining is explained, inter alia, with methods describing the individual phases of the process of knowledge discovery in databases (CRISP-DM, SEMMA). There is also information about possible practical applications, technologies and products available in the market (both products available free and commercial products). Introduction of the main data mining methods and specific algorithms (decision trees, association rules, neural networks and other methods) serves as a theoretical introduction, on which are the practical applications of real data in real health insurance companies build. These are applications seeking the causes of increased remittances and churn prediction. I have solved these applications in freely-available systems Weka and LISP-Miner. The objective is to introduce and to prove data mining capabilities over this type of data and to prove capabilities of Weka and LISP-Miner systems in solving tasks due to the methodology CRISP-DM. The last part of thesis is devoted the fields of cloud and grid computing in conjunction with data mining. It offers an insight into possibilities of these technologies and their benefits to the technology of data mining. Possibilities of cloud computing are presented on the Amazon EC2 system, grid computing can be used in Weka Experimenter interface.
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