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Advanced Data Mining in Cardiology
Mézl, Martin ; Provazník, Ivo (referee) ; Sekora, Jiří (advisor)
The aim of this master´s thesis is to analyse and search unusual dependencies in database of patients from Internal Cardiology Clinic Faculty Hospital Brno. The part of the work is theoretical overview of common data mining methods used in medicine, especially decision trees, naive Bayesian classifier, artificial neural networks and association rules. Looking for unusual dependencies between atributes is realized by association rules and naive Bayesian classifier. The output of this work is a complex system for Knowledge discovery in databases process for any data set. This work was realized with collaboration of Internal Cardiology Clinic Faculty Hospital Brno. All programs were made in Matlab 7.0.1.
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Data comparability in knowledge discovery in databases
Horáková, Linda ; Chudán, David (advisor) ; Svátek, Vojtěch (referee)
The master thesis is focused on analysis of data comparability and commensurability in datasets, which are used for obtaining knowledge using methods of data mining. Data comparability is one of aspects of data quality, which is crucial for correct and applicable results from data mining tasks. The aim of the theoretical part of the thesis is to briefly describe the field of knowledqe discovery and define specifics of mining of aggregated data. Moreover, the terms of comparability and commensurability is discussed. The main part is focused on process of knowledge discovery. These findings are applied in practical part of the thesis. The main goal of this part is to define general methodology, which can be used for discovery of potential problems of data comparability in analyzed data. This methodology is based on analysis of real dataset containing daily sales of products. In conclusion, the methodology is applied on data from the field of public budgets.
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Data mining
Dolejšek, Jakub ; Peliš, Michal (advisor) ; Verner, Jonathan (referee)
Data mining Bc. thesis Jakub Dolejšek (english abstract) This paper describes problematic of the knowleadge database discovery with focus on methods of decision trees and neural networks with examples of their application on concrete examples. Powered by TCPDF (www.tcpdf.org)
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Advanced Data Mining in Cardiology
Mézl, Martin ; Provazník, Ivo (referee) ; Sekora, Jiří (advisor)
The aim of this master´s thesis is to analyse and search unusual dependencies in database of patients from Internal Cardiology Clinic Faculty Hospital Brno. The part of the work is theoretical overview of common data mining methods used in medicine, especially decision trees, naive Bayesian classifier, artificial neural networks and association rules. Looking for unusual dependencies between atributes is realized by association rules and naive Bayesian classifier. The output of this work is a complex system for Knowledge discovery in databases process for any data set. This work was realized with collaboration of Internal Cardiology Clinic Faculty Hospital Brno. All programs were made in Matlab 7.0.1.
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The Real Knowledge Discovery Task
Kolafa, Ondřej ; Berka, Petr (advisor) ; Kliegr, Tomáš (referee)
The major objective of this thesis is to perform a real data mining task of classifying term deposit accounts holders. For this task an anonymous bank customers with low funds position data are used. In correspondence with CRISP-DM methodology the work is guided through these steps: business understanding, data understanding, data preparation, modeling, evaluation and deployment. The RapidMiner application is used for modeling. Methods and procedures used in actual task are described in theoretical part. Basic concepts of data mining with special respect to CRM segment was introduced as well as CRISP-DM methodology and technics suitable for this task. A difference in proportions of long term accounts holders and non-holders enforced data set had to be balanced in favour of holders. At the final stage, there are twelve models built. According to chosen criterias (area under curve and f-measure) two best models (logistic regression and bayes network) were elected. In the last stage of data mining process a possible real-world utilisation is mentioned. The task is developed only in form of recommendations, because it can't be applied to the real situation.
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Analysis of real data from Alza.cz product department using methods of KDD
Válek, Martin ; Berka, Petr (advisor) ; Kliegr, Tomáš (referee)
This thesis deals with data analysis using methods of knowledge discovery in databases. The goal is to select appropriate methods and tools for implementation of a specific project based on real data from Alza.cz product department. Data analysis is performed by using association rules and decision rules in the Lisp-Miner and decision trees in the RapidMiner. The methodology used is the CRISP-DM. The thesis is divided into three main sections. First section is focused on the theoretical summary of information about KDD. There are defined basic terms and described the types of tasks and methods of KDD. In the second section is introduced the methodology CRISP-DM. The practical part firstly introduces company Alza.cz and its goals for this task. Afterwards, the basic structure of the data and preparation for the next step (data mining) is described. In conclusion, the results are evaluated and the possibility of their use is outlined.
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The real application of methods knowledge discovery in databases on practical data
Mansfeldová, Kateřina ; Máša, Petr (advisor) ; Kliegr, Tomáš (referee)
This thesis deals with a complete analysis of real data in free to play multiplayer games. The analysis is based on the methodology CRISP-DM using GUHA method and system LISp-Miner. The goal is defining player churn in pool from Geewa ltd.. Practical part show the whole process of knowledge discovery in databases from theoretical knowledge concerning player churn, definition of player churn, across data understanding, data extraction, modeling and finally getting results of tasks. In thesis are founded hypothesis depending on various factors of the game.
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Automation of data preprocessing using domain knowledge
Beskyba, Jan ; Šimůnek, Milan (advisor) ; Pejčoch, David (referee)
In this work we propose a solution that would help automate the part of knowledge discovery in databases. Domain knowledge has an important role in the automation process which is necessary to include into the proposed program for data preparation. In the introduction to this work, we focus on the theoretical basis of knowledge discovery of databases with an emphasis on domain knowledge. Next, we focus on the basic principles of data pre-processing and scripting language LMCL that could be part of the design of the newly established applications for automated data preparation. Subsequently, we will deal with application design for data pre-processing, which will be verified on the data the House of Commons.
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Application of knowledge discovery methods in the field of cardiac surgery
Čech, Bohuslav ; Berka, Petr (advisor) ; Aiglová, Květoslava (referee)
This theses demonstrate practical use of knowledge discovery in the field of cardiac surgery. The tasks of the Department of Cardiac Surgery University Hospital Olomouc are solved through the use of GUHA method and LISp-Miner system. Mitral valve surgery data comes from clinical practice between the years 2002 and 2011. Theoretical part includes chapter on KDD -- type of tasks, methods and methodology and chapter on cardiac surgery -- anatomy and functions of heart, mitral valve disease and diagnostic methods including quantification. Practical part brings solutions of the tasks and whole process is described in the spirit of CRISP-DM.
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