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Sequential Pattern Mining
Tisoň, Zdeněk ; Zendulka, Jaroslav (referee) ; Hlosta, Martin (advisor)
This master's thesis is focused on knowledge discovery from databases, especially on methods of mining sequential patterns. Individual methods of mining sequential patterns are described in detail. Further, this work deals with extending the platform Microsoft SQL Server Analysis Services of new mining algorithms. In the practical part of this thesis, plugins for mining sequential patterns are implemented into MS SQL Server. In the last part, these algorithms are compared on different data sets.
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Application for Management and Analysis of Data About Games of Chess
Kunčar, Petr ; Šebek, Michal (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with design and implementation of system for management and analysis of data about games of chess. The games of chess are stored in a database, to which they are uploaded by means of the system. There is also the AprioriAll algorithm implemented to find sequential patterns occuring in the data. Except of mining sequential patterns, the functionality of tree creation and searching in the data is also implemented. The data about games of chess are stored in a form of standard PGN files.
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Application for Management and Analysis of Data About Games of Chess
Kunčar, Petr ; Šebek, Michal (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with design and implementation of system for management and analysis of data about games of chess. The games of chess are stored in a database, to which they are uploaded by means of the system. There is also the AprioriAll algorithm implemented to find sequential patterns occuring in the data. Except of mining sequential patterns, the functionality of tree creation and searching in the data is also implemented. The data about games of chess are stored in a form of standard PGN files.
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Sequential Pattern Mining
Tisoň, Zdeněk ; Zendulka, Jaroslav (referee) ; Hlosta, Martin (advisor)
This master's thesis is focused on knowledge discovery from databases, especially on methods of mining sequential patterns. Individual methods of mining sequential patterns are described in detail. Further, this work deals with extending the platform Microsoft SQL Server Analysis Services of new mining algorithms. In the practical part of this thesis, plugins for mining sequential patterns are implemented into MS SQL Server. In the last part, these algorithms are compared on different data sets.
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Web Analytics: Identification of new trends
Slavík, Michal ; Kliegr, Tomáš (advisor) ; Nekvasil, Marek (referee)
The goal of this thesis is to identify the main trends in the field of tools used to analyse web traffic. The necessary theoretical background is extracted from relevant literature and field research is chosen to gain knowledge of practitioners. Following trends have been identified: a growth in demand for Web Analytics software, an increasing interest in Web Analytics courses, an enlargment of measuring Web 2.0 and social networks, use of semantic information as the most fruitful section of academic research. The thesis also presents the main techniques of Web Usage Mining: association rules, sequential patterns, and clustering. A section about query categorization is also included. According to the field research, practitioners express most interest in clustering. The first two chapters present Web Analytics in general and introduce the main aspects of current applications. The third chapter covers theoretical research, the fifth one presents results of the field research. The fourth chapter raises the point that terminology of Web Analytics is not unified.
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