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Association Rule Mining
Šabatka, Ondřej ; Stryka, Lukáš (referee) ; Bartík, Vladimír (advisor)
This bachelor's thesis is concerned with the association rule mining. The first part is devoted to the explanation of data mining technology and theory, which are necessary pre-steps for getting acquainted with association analysis. The next part focuses on the association analysis itself and explains the principals of algorithm Apriori in detail. The last part of the thesis describes the implementation and testing of algorithm Apriori in the Java programming language.
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Identifying Entity Types Based on Information Extraction from Wikipedia
Rusiňák, Petr ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This paper presents a system for identifying entity types of articles on Wikipedia (e.g. people or sports events) that can be used for identifaction of any arbitrary entity. The~input files for this system are a list of several pages that belong to this entity and a list of several pages that do not belong to this entity. These lists will be used to generate features that can be used for generation of the list of all pages belonging to this entity. The fatures can be based on both structured information on Wikipedia such as templates and categories and non-structured informations found by the analysis of natural text in the first sentence of the article where a defining noun that represents what the article is about will be found. This system support pages written in Czech and English and can be extended to support other languages.
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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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Identifying Entity Types Based on Information Extraction from Wikipedia
Rusiňák, Petr ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This paper presents a system for identifying entity types of articles on Wikipedia (e.g. people or sports events) that can be used for identifaction of any arbitrary entity. The~input files for this system are a list of several pages that belong to this entity and a list of several pages that do not belong to this entity. These lists will be used to generate features that can be used for generation of the list of all pages belonging to this entity. The fatures can be based on both structured information on Wikipedia such as templates and categories and non-structured informations found by the analysis of natural text in the first sentence of the article where a defining noun that represents what the article is about will be found. This system support pages written in Czech and English and can be extended to support other languages.
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Association Rule Mining
Šabatka, Ondřej ; Stryka, Lukáš (referee) ; Bartík, Vladimír (advisor)
This bachelor's thesis is concerned with the association rule mining. The first part is devoted to the explanation of data mining technology and theory, which are necessary pre-steps for getting acquainted with association analysis. The next part focuses on the association analysis itself and explains the principals of algorithm Apriori in detail. The last part of the thesis describes the implementation and testing of algorithm Apriori in the Java programming language.
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|
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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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Data Mining in K2 Information System
Figura, Petr ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This project was originated by K2 atmitec Brno s.r.o. company. The result is data mining module in K2 information system environment. Engineered data module implements association analysis over the data of K2 information system data warehouse. Analyzed data contains information about sales filed in K2 information system. Module is implementing consumer basket analysis.
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