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Periodic Patterns Mining
Stríž, Rostislav ; Zendulka, Jaroslav (referee) ; Šebek, Michal (advisor)
Data collecting and analysis are commonly used techniques in many sectors of today's business and science. Process called Knowledge Discovery in Databases presents itself as a great tool to find new and interesting information that can be used in a future developement. This thesis deals with basic principles of data mining and temporal data mining as well as with specifics of concrete implementation of chosen algorithms for mining periodic patterns in time series. These algorithms have been developed in a form of managed plug-ins for Microsoft Analysis Services -- service that provides data mining features for Microsoft SQL Server. Finally, we discuss obtained results of performed experiments focused on time complexity of implemented algorithms.
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Multi-Level Association Rule Mining
Mičulka, Václav ; Kupčík, Jan (referee) ; Hlosta, Martin (advisor)
This bachelor thesis deals with multi-level association rules mining and implementation of this functionality as a plug-in to the Microsoft Analysis Services. In the beginning, the data mining is analysed and then the thesis deals with the assosiation analysis. After the theoretical and implementation aspects, algorithms ML T2L1 and the derived algorithm for level-crossing association rule mining are analysed. Subsequently, performance testing was performed. The achieved results are summed up at the end of the thesis.
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Multi-Level Association Rule Mining
Mičulka, Václav ; Kupčík, Jan (referee) ; Hlosta, Martin (advisor)
This bachelor thesis deals with multi-level association rules mining and implementation of this functionality as a plug-in to the Microsoft Analysis Services. In the beginning, the data mining is analysed and then the thesis deals with the assosiation analysis. After the theoretical and implementation aspects, algorithms ML T2L1 and the derived algorithm for level-crossing association rule mining are analysed. Subsequently, performance testing was performed. The achieved results are summed up at the end of the thesis.
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Periodic Patterns Mining
Stríž, Rostislav ; Zendulka, Jaroslav (referee) ; Šebek, Michal (advisor)
Data collecting and analysis are commonly used techniques in many sectors of today's business and science. Process called Knowledge Discovery in Databases presents itself as a great tool to find new and interesting information that can be used in a future developement. This thesis deals with basic principles of data mining and temporal data mining as well as with specifics of concrete implementation of chosen algorithms for mining periodic patterns in time series. These algorithms have been developed in a form of managed plug-ins for Microsoft Analysis Services -- service that provides data mining features for Microsoft SQL Server. Finally, we discuss obtained results of performed experiments focused on time complexity of implemented algorithms.
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