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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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Knowledge Discovery from Time Series
Krutý, Peter ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis is focused on the field of knowledge discovery from data, specifically from time series. Main objective is to research Python programming language support in this area and then design and implement an application that will allow to demonstrate and compare selected methods. Methods are demonstrated in experiments using appropriate data set. The output of the thesis is a comparison of methods for specific tasks and the application implementing selected methods.
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Knowledge Discovery from Databases with Use of the R Language
Krutý, Peter ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This thesis is focused on the field of knowledge discovery from databases. Main objective is to research possibilities of R language and its support in this area. Support is researched by experiments using appropriate data sets. More detailed attention is given to the methods of classification, clustering and association rules learning. The output of the thesis is comparison of methods application in R and defining the suitability of using language for knowledge discovery from databases.
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Association Rules Mining
Dvořák, Michal ; Chmelař, Petr (referee) ; Stryka, Lukáš (advisor)
The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.
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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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Knowledge Discovery from Time Series
Krutý, Peter ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis is focused on the field of knowledge discovery from data, specifically from time series. Main objective is to research Python programming language support in this area and then design and implement an application that will allow to demonstrate and compare selected methods. Methods are demonstrated in experiments using appropriate data set. The output of the thesis is a comparison of methods for specific tasks and the application implementing selected methods.
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Data mining of the database of Consulting centre for metabolism disorders
Senft, Martin ; Ivánek, Jiří (advisor) ; Musil, Vladimír (referee)
This thesis applies the data mining method of decision rules on data from Consulting centre for Metabolism disorders from University hospital Pilsen. As a tool is used the system LISp-Miner, developed at University of Economics, Prague. Decision rules found are evaluated by a specialist. The main parts of this thesis are followings: an overview on main data mining methods and results evalutation methods, description of the data mining method application on data and description and evaluation of results.
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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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Knowledge Discovery from Databases with Use of the R Language
Krutý, Peter ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This thesis is focused on the field of knowledge discovery from databases. Main objective is to research possibilities of R language and its support in this area. Support is researched by experiments using appropriate data sets. More detailed attention is given to the methods of classification, clustering and association rules learning. The output of the thesis is comparison of methods application in R and defining the suitability of using language for knowledge discovery from databases.
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Data mining of the database of Consulting centre for metabolism disorders
Senft, Martin ; Ivánek, Jiří (advisor) ; Musil, Vladimír (referee)
This thesis applies the data mining method of decision rules on data from Consulting centre for Metabolism disorders from University hospital Pilsen. As a tool is used the system LISp-Miner, developed at University of Economics, Prague. Decision rules found are evaluated by a specialist. The main parts of this thesis are followings: an overview on main data mining methods and results evalutation methods, description of the data mining method application on data and description and evaluation of results.
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