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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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OLAP Recommender
Koukal, Bohuslav ; Chudán, David (advisor) ; Vojíř, Stanislav (referee)
Manual data exploration in data cubes and searching for potentially interesting and useful information starts to be time-consuming and ineffective from certain volume of the data. In my thesis, I designed, implemented and tested a system, automating the data cube exploration and offering potentially interesting views on OLAP data to the end user. The system is based on integration of two data analytics methods - OLAP analysis data visualisation and data mining, represented by GUHA association rules mining. Another contribution of my work is a research of possibilities how to solve differences between OLAP analysis and association rule mining. Implemented solutions of the differences include data discretization, dimensions commensurability, design of automatic data mining task algorithm based on the data structure and mapping definition between mined association rules and corresponding OLAP visualisation. The system was tested with real retail sales data and with EU structural funds data. The experiments proved that complementary usage of the association rule mining together with OLAP analysis identifies relationships in the data with higher success rate than the isolated use of both techniques.
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Using system LISp-Miner for large real data
Hrnčíř, Jan ; Rauch, Jan (advisor) ; Chudán, David (referee)
This dissertation thesis describes an advanced method of knowledge discovery in databases (KDD), implemented in system LISp-Miner. The goal is to show the possibilities of coordinated use of analytical tools and complex procedures GUHA in this system. The thesis uses methodology CRISP-DM, which is firstly described and work is proceeded using this methodology in the following sections. The author firstly introduces readers domain area and then the data itself, which are processed to the analysis needs. Analytical questions that are answered at, are drawn from the literature, which is focused on domain area. The work should be used as a guide to LISp-Miner users, using analytical tools and procedures GUHA is therefore described the easiest way to understand.
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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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Analysis of real data for Customer Services Department
Maximilián, Michal ; Šimůnek, Milan (advisor) ; Veselý, Jiří (referee)
The goal of this bachelor thesis is to find certain relationships by analyzing real CRM data. These relationships would then be used to specify a draft of content of companys new webside. The analysis will be completed through CF-Miner and KL-Miner procedures, which are procedures of LISp-Miner system, which is an academic system for Knowledge Discovery in Databases, based on the GUHA method. The whole analysis process is divided according to the phases of the CRISP-DM methodology. The contribution of this thesis is primarily to find unknown relationships and dependencies, which will be effectively used in real life, along with the introduction of methods and techniques used in the analysis, and last, but not least, the introduction of LISp-Miner system itself. The thesis is divided into a theoretical and empirical sections. In the first three chapters, I will explain what is meant by Knowledge Discovery in Databases and what techniques, methodologies and procedures are used during this process. Further, I will explain individual phases of KDD corresponding to the CRISP-DM methodology. Towards the end of the theoretical part, I will describe LISp-Miner system that has been used for this analysis. The empirical section is divided according to the CRISP-DM methodology, where I will first introduce the scope and the data that will be analyzed. In further steps, I will prepare the analyzed data and use them to solve analytical problems. At the end of the empirical part, I will interpret the results of individual analyses and suggest use in real life.
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Options of presentation of KDD results on Web
Koválik, Tomáš ; Rauch, Jan (advisor) ; Šimůnek, Milan (referee)
This diploma thesis covers KDD analysis of data and options of presentation of KDD results on Web. The paper is divided into three main sections, which follow the whole process of this thesis. In the first section are mentioned theoretical basics needed for understanding of discussed problem. In this section are described notions data matrix and domain knowledge, concept of CRISP-DM methodology, GUHA method, system LISp-Miner and implementation of GUHA method in LISp-Miner including description of core procedures 4ft-Miner and CF-Miner. The second section is dedicated to the first goal of this paper. It briefly summarizes analysis made during pre-analysis phase. Then is described process of analysis of domain knowledge in a given data set. The third part focuses on the second goal of this thesis, which is problem of presentation of KDD results on Web. This section covers brief theoretical basis for used technologies. Then is described development of export script for automatic generation of website from results found using LISp-Miner system including description of structure of the output and recommendations for work in LISp-Miner system.
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Use of data mining techniques for open data
Prokůpek, Miroslav ; Rauch, Jan (advisor) ; Chudán, David (referee)
This diploma thesis examines applications of datamining methods to open data. It is realized by solving analytical questions using the LISp-Miner system. Analytical questions are examined in data from The Czech Trade Inspection Authority from the perspective of the data owner. Procedure used to solve analytical questions is 4ft-Miner. There are presented and resolved four analytical questions, which are the results of the work. Work includes a detailed description of the transformation of the relational database into a format suitable for data mining. A detailed description of the data is also included. The theoretical part deals with the GUHA method and CRISP-DM methodology.
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Analýza dát z oblasti kontroly kvality použitím systému LISp-Miner
Štefke, Martin ; Šimůnek, Milan (advisor) ; Srogoňová, Kristína (referee)
Objective of the bachelor thesis is analysis of occurrence of non-conforming products in SEWS Slovakia. There were analyzed production defects from the period January 2013 to October 2014, the analysis was perform from the database in the academic system LISp-Miner. In the initial theoretical part is a summary of the different approaches to the issue of knowledge discovery from databases.The following practical part is described the treatment and processing of data,define the basic analytic issues. At the end there are defined relevant relationship betweendata and analytical methods.
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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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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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