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Srovnání vybraných nástrojů dobývání znalostí z databází z hlediska implementace asociačních pravidel
Lízler, Robert ; Nekvapil, Viktor (advisor) ; Rauch, Jan (referee)
This bachelor thesis deals with a comparison between two selected data mining software tools, LISp-Miner, developed at the department of information and knowledge engineering at the faculty of informatics and statistics of the University of Economics, Prague, and Rapidminer, a globally popular software suite. The focus of the comparison is mining for association rules. The aim of this work is first to provide a user-oriented evaluation of how the software tools compare in the selected area and second to attempt to discover some interesting differences between the results of how the software tools implement association mining procedures. To reach these goals, the software tools will be tested and evaluated by the author according to a selected set of criteria, grouped into 3 categories: functionality, usability and performance. The work is structured as follows: chapter 1 sets out some theoretical background and introduces the software tools, chapter 2 evaluates the available functionality of the tools for various steps of the overall association mining procedure, chapter 3 rates the software tools based on their usability and user-friendliness, while chapter 4 summarises the results of testing the software tools on a selected data set.
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Data mining v oblasti Customer relationship managemet: Analýza dat významné logistické společnosti
Nekvapil, Viktor ; Rauch, Jan (advisor) ; Šimůnek, Milan (referee)
The thesis addresses possibilities of deploying the open source data mining system LISp-Miner in the customer relationship management (CRM), specifically in the area of lead management. This is basically a process of finding information about potential customers, qualifying those customers according to their potential (future value), and turning the selected potential customers to real customers. The data used includes the records concerning the lead management of a major logistic company operating worldwide (the company wanted to stay in anonymity). The data is analysed using the LISp-Miner system which is an academic software developed at the Faculty of Informatics and Statistics at the University of Economics, Prague. The thesis also pays attention to the collaboration with the business experts of the company which provided the data. The principle aim of the thesis is to provide information contributing to the possible change of internal processes of the company. Further aims are to propose directions of the use of the LISp-Miner system when solving a similar data mining task, and propose a simple and understandable way how to present the results. The aims have been achieved by doing the analysis in compliance with the CRISP-DM Methodology. The asset of the thesis is the description of the whole project which includes the analysis of real data using the LISp-Miner system. Further result of the thesis is the description of the lead management domain. Finally, the thesis offers the instructions and recommendations for future similar projects. Section I outlines the LISp-Miner system and its procedures. Section II - A case study describes the process of analysing the data. Two cycles ("iterations") of the analysis were performed. The chapters devoted to the both iterations have been structured according to the phases of the CRISP-DM Methodology. Section III summarises the observations gained during the entire project. Moreover, it gives recommendations and instructions for the application in case a similar project of the data analysis using the LISp-Miner system is designed.
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Aplikace procedury Ac4ft-Miner na medicínská data
Nekvapil, Viktor ; Rauch, Jan (advisor) ; Šimůnek, Milan (referee)
This bachelor thesis deals with the data mining procedure Ac4ft-Miner, implemented in the LISp-Miner system, which is developed at the Department of Information and Knowledge Engineering at the University of Economics, Prague. The aim of this thesis is firstly to describe the procedure in a simple, understandable way. Secondly, the aim is to apply this procedure on the medical data and present examples of use of this procedure. Further aim is to create methodology of use for doctors from the experience obtained. The aims are reached by using a lot of examples, which demonstrate theoretical concepts on concrete data and by the pursuit of the simple visualisation of tasks (analytical questions) solved by the procedure. The output of this thesis is a coherent text with lot of examples separated from the continuous text; so the reader familiar with a particular topic can skip the examples and proceed to the next issue. Further result of this thesis is an outline of the graphical presentation of analytical questions. Both the examples and the graphical presentation will be used further in the SEWEBAR project of which this thesis is one part. The methodology of use of the procedure for doctors is in the form of advices for use of the tool which should contribute to the further research which is needed. This is because of the high complexity of the procedure, which does not allow formulating general conclusions usable in the methodology. Chapter 1 characterizes the overall process of Knowledge Discovery in Databases represented by the CRISP-DM Methodology. Chapter 2 presents theoretical concepts related to Ac4ft-Miner. Chapter 3 deals with action rules. Chapter 4 addresses possibilities of defining the input and interpretation of the output of the Ac4ft-Miner. Chapter 5 describes the research conducted on the real medical data set ADAMEK, states methodology and examples of the output. Chapter 6 summarises the experience obtained and formulates the methodology of use of Ac4ft-Miner for doctors.
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