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Use of Sequential Pattern Mining in Google Analytics
Viskievič, Gergő ; Šabatka, Pavel (referee) ; Luhan, Jan (advisor)
The bachelor thesis focuses on the design and development of an algorithm for sequential pattern mining in Google Analytics 4 data. Presents and analyzes available algorithms for sequential pattern mining. Analyzes the data model and the use of Google Analytics 4. Based on the requirements of business processes, the algorithm is proposed suitable for the expected input data.
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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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Methods for Mining Sequential Patterns
Fekete, Martin ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Sequential pattern mining is a field of data mining with wide applications. Currently, there are a number of algorithms and approaches to the problem of sequential pattern mining. The aim of this work is to design and implement an application designed for sequential pattern mining and use it to experimentally compare the chosen algorithms. Experiments are performed with both synthetic and real databases. The output of the work is a summary of the advantages and disadvantages of each algorithm for different kinds of input databases and an application implementing the selected algorithms of the SPMF library.
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Use of Sequential Pattern Mining in Google Analytics
Viskievič, Gergő ; Šabatka, Pavel (referee) ; Luhan, Jan (advisor)
The bachelor thesis focuses on the design and development of an algorithm for sequential pattern mining in Google Analytics 4 data. Presents and analyzes available algorithms for sequential pattern mining. Analyzes the data model and the use of Google Analytics 4. Based on the requirements of business processes, the algorithm is proposed suitable for the expected input data.
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|
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Methods for Mining Sequential Patterns
Fekete, Martin ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Sequential pattern mining is a field of data mining with wide applications. Currently, there are a number of algorithms and approaches to the problem of sequential pattern mining. The aim of this work is to design and implement an application designed for sequential pattern mining and use it to experimentally compare the chosen algorithms. Experiments are performed with both synthetic and real databases. The output of the work is a summary of the advantages and disadvantages of each algorithm for different kinds of input databases and an application implementing the selected algorithms of the SPMF library.
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|
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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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