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The Betting Agent
Bělohlávek, Jiří ; Rozman, Jaroslav (referee) ; Grulich, Lukáš (advisor)
This master thesis deals with design and implementation of betting agent. It covers issues such as theoretical background of an online betting, probability and statistics. In its first part it is focused on data mining and explains the principle of knowledge mining form data warehouses and certain methods suitable for different types of tasks. Second, it is concerned with neural networks and algorithm of back-propagation. All the findings are demonstrated on and supported by graphs and histograms of data analysis, made via SAS Enterprise Miner program. In conclusion, the thesis summarizes all the results and offers specific methods of extension of the agent.
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Comparation of Models for Datamining
Pospíšil, Jan ; Bartík, Vladimír (referee) ; Lukáš, Roman (advisor)
This thesis focuses on comparing of the datamining models features depending on the different databazis topology. The objekt was to find key features that at most involve the accuracy of classification. Thesis is composed from chapters in a way that even a non-professional or even a complete laik could understand the object and could find theese thesis results useful. In the beginning the reader is beeing made familiar with all the background information about datamining and its models and algorithms, the second part denotes about the model comparison and discusses its results.
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Comparation of Models for Datamining
Gabriš, Ondrej ; Stryka, Lukáš (referee) ; Burgetová, Ivana (advisor)
Increasing development of information technology causes the amount of produced data to grow continously. And so the need becomes more intensive to process the produced data fast and efficiently to discover hidden knowledge contained in the data. This thesis examines the process of knowledge discovery in data, it's particular phases, various methods for mining the data and their comparation. Models of regression, neural network and decision tree are analysed in detail. The thesis also introduces one of the leading tools for datamining the SAS Enterprise Miner and demonstrates it's practical application on data. The purpose of this thesis is comparation of models for datamining in the SAS Enterprise Miner environment, discussion of the results and analysis to determine which model is suitable for different kinds of mined data.
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The Betting Agent
Bělohlávek, Jiří ; Rozman, Jaroslav (referee) ; Grulich, Lukáš (advisor)
This master thesis deals with design and implementation of betting agent. It covers issues such as theoretical background of an online betting, probability and statistics. In its first part it is focused on data mining and explains the principle of knowledge mining form data warehouses and certain methods suitable for different types of tasks. Second, it is concerned with neural networks and algorithm of back-propagation. All the findings are demonstrated on and supported by graphs and histograms of data analysis, made via SAS Enterprise Miner program. In conclusion, the thesis summarizes all the results and offers specific methods of extension of the agent.
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Creation of Unit for Datamining
Krásenský, David ; Burgetová, Ivana (referee) ; Lukáš, Roman (advisor)
The goal of this work is to create data mining module for information system Belinda. Data from database of clients will be analyzed using SAS Enterprise Miner. Results acquired using several data mining methods will be compared. During the second phase selected data mining method will be implemented such as module of information system Belinda. The final part of this work is evaluation of acquired results and possibility of using this module.
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Comparation of Models for Datamining
Pospíšil, Jan ; Bartík, Vladimír (referee) ; Lukáš, Roman (advisor)
This thesis focuses on comparing of the datamining models features depending on the different databazis topology. The objekt was to find key features that at most involve the accuracy of classification. Thesis is composed from chapters in a way that even a non-professional or even a complete laik could understand the object and could find theese thesis results useful. In the beginning the reader is beeing made familiar with all the background information about datamining and its models and algorithms, the second part denotes about the model comparison and discusses its results.
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Predictive Analytics - Process and Development of Predictive Models
Praus, Ondřej ; Pour, Jan (advisor) ; Mrázek, Luboš (referee)
This master's degree thesis focuses on predictive analytics. This type of analysis uses historical data and predictive models to predict future phenomenon. The main goal of this thesis is to describe predictive analytics and its process from theoretical as well as practical point of view. Secondary goal is to implement project of predictive analytics in an important insurance company operating in the Czech market and to improve the current state of detection of fraudulent insurance claims. Thesis is divided into theoretical and practical part. The process of predictive analytics and selected types of predictive models are described in the theoretical part of the thesis. Practical part describes the implementation of predictive analytics in a company. First described are techniques of data organization used in datamart development. Predictive models are then implemented based on the data from the prepared datamart. Thesis includes examples and problems with their solutions. The main contribution of this thesis is the detailed description of the project implementation. The field of the predictive analytics is better understandable thanks to the level of detail. Another contribution of successfully implemented predictive analytics is the improvement of the detection of fraudulent insurance claims.
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An Empirical Comparison of Commercial Data Mining Tools
Faruzel, Petr ; Berka, Petr (advisor) ; Máša, Petr (referee)
The presented work "An Empirical Comparison of Commercial Data Mining Tools" deals with data mining tools from world's leading software providers of statistical solutions. The aim of this work is to compare commercial packages IBM SPSS Modeler and SAS Enterprise Miner in terms of their specification and utility considering a chosen set of evaluation criteria. I would like to achieve the appointed goal by a detailed analysis of selected features of the surveyed software packages as well as by their application on real data. The comparison is founded on 29 component criteria which reflect user's requirements regarding functionality, usability and flexibility of the system. The pivotal part of the comparative process is based on an application of the surveyed data mining tools on data concerning meningoencephalitis. Results predestinate evaluation of their performance while analyzing small and large data. Quality of developed data models and duration of their derivation are stated in reference to the use of six comparable data mining techniques for classification. Small data more likely comply with IBM SPSS Modeler. Although it produces slightly less accurate models, their development times are much shorter. Increasing the amount of data changes the situation in favor of competition. SAS Enterprise Miner manages better results while analyzing large data. Considerably more accurate models are accompanied by slightly shorter times of their development. Functionality of the surveyed data mining tools is comparable, whereas their usability and flexibility differentiate. IBM SPSS Modeler offers apparently better usability and learnability. Users of SAS Enterprise Miner have a slightly more flexible data mining tool at hand.
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