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Knowledge Discovery from Data of an Insurance Company
Kříž, Ondřej ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with the issue of knowledge discovery from databases. Its aim is to compile algorithmically processable datasets from operational data of an unnamed insurance company, which will subsequently be analyzed by functions of the scikit-learn library in the Python language using various classification algorithms and the FP-growth algorithm in the area of creating strong association rules and subsequent evaluation of results.
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Mining Multiple Level Association Rules
Nguyenová, Thanh Lam ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with multiple level association rules mining. The aim of this work is to focus on available algorithms for mining multiple level association rules and to implement an application with a graphical user interface that will demonstrate the functionality of these algorithms. Five algorithms based on the Apriori algorithm were chosen. Experiments with each algorithm were performed using the application and the results were compared and evaluated at the end of the thesis.
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Business Process Mining
Skácel, Jan ; Kreslíková, Jitka (referee) ; Bartík, Vladimír (advisor)
This thesis explains business process mining and it's principles. A substantial part is devoted to the problems of process discovery. Further, based on the analysis of specific manufacturing process are proposed three methods that are trying to identify shortcomings in the process. First discovers the manufacturing process and renders it into a graph. The second method uses simulator of production history to obtain products that may caused delays in the process. Acquired data are used to mine frequent itemsets. The third method tries to predict processing time on the selected workplace using asociation rules. Last two mentioned methods employ an algorithm Frequent Pattern Growth. The knowledge obtained from this thesis improve efficiency of the manufacturing process and enables better production planning.
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Knowledge Discovery in Text
Smékal, Luděk ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This MSc Thesis handles with so-called data mining. Data mining is about obtaining some data or informations from databases, where these data or informations are not directly visible, but they are accessible by using special algorithms. This MSc Thesis mainly aims documents clasifying by selected method in scope of digital library. The selected method is based on sets of items called "itemsets method". This method extends Apriori algorithm application field originally designed for transaction databases processing and generation of sets of frequented items.
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Mining Multiple Level Association Rules
Nguyenová, Thanh Lam ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with multiple level association rules mining. The aim of this work is to focus on available algorithms for mining multiple level association rules and to implement an application with a graphical user interface that will demonstrate the functionality of these algorithms. Five algorithms based on the Apriori algorithm were chosen. Experiments with each algorithm were performed using the application and the results were compared and evaluated at the end of the thesis.
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Knowledge Discovery in Text
Smékal, Luděk ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This MSc Thesis handles with so-called data mining. Data mining is about obtaining some data or informations from databases, where these data or informations are not directly visible, but they are accessible by using special algorithms. This MSc Thesis mainly aims documents clasifying by selected method in scope of digital library. The selected method is based on sets of items called "itemsets method". This method extends Apriori algorithm application field originally designed for transaction databases processing and generation of sets of frequented items.
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Business Process Mining
Skácel, Jan ; Kreslíková, Jitka (referee) ; Bartík, Vladimír (advisor)
This thesis explains business process mining and it's principles. A substantial part is devoted to the problems of process discovery. Further, based on the analysis of specific manufacturing process are proposed three methods that are trying to identify shortcomings in the process. First discovers the manufacturing process and renders it into a graph. The second method uses simulator of production history to obtain products that may caused delays in the process. Acquired data are used to mine frequent itemsets. The third method tries to predict processing time on the selected workplace using asociation rules. Last two mentioned methods employ an algorithm Frequent Pattern Growth. The knowledge obtained from this thesis improve efficiency of the manufacturing process and enables better production planning.
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