04878ntm a22005537i 4500 000553817 CZ-PrVSE 20190602111310.0 m d cr n|||||||||| 190602s2019 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Nekvapil, Viktor ISIS:50819 dis Automatizace deskriptivního data miningu : využití externích dat v evaluační fázi eng Automation of Descriptive Data Mining : The Use of External Data in the Evaluation Phase Viktor Nekvapil 2019 ?? stran : digital, PDF soubor Vedoucí práce: Jan Rauch Disertační práce (Ph.D.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2019 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2019 Data mining has reached a mature state. There is a plethora of algorithms available, used more and more in everyday business. As far as the descriptive data mining is concerned, its assets are not clear but it often represents a necessary first step in the predictive task. Therefore, there is a distinct requirement to automate the descriptive task and thus reduce costs which are not easily justifiable considering the perspective of business people. This thesis strives to contribute to this issue.The thesis focuses on an automation of descriptive data mining task, specifically, the evaluation phase. Automation of data mining requires encoding a significant amount of domain knowledge. The thesis tries to use cheaper sources of domain knowledge than the domain experts source, that is, processed external data (External Knowledge), which can be either internal data of a company or publicly available resources, such as open data.The main aim of the thesis is to propose a new way of automation of the evaluation phase of descriptive data mining task based on external data. The thesis provides a comprehensive overview of data mining, domain knowledge and automation. The newly proposed External Knowledge Framework includes two approaches to the utilization of External Knowledge. The first approach, called Explanation System, offers the user an additional knowledge that could help him in the evaluation of the results. The second approach, referred to as SEI-formulas, further automates the evaluation phase of the descriptive data mining task and prepares automatic conclusions based on the consequences or contradictions of the defined SEI-formula. SEI-formula is a pre-defined relationship of the attribute from the resulting pattern and the attribute from External Knowledge.Both Explanation system and the SEI-formulas are implemented making use of custom-made Python engine; SQLite database is used as a storage of External Knowledge. The programming codes of implementation are publicly available at GitHub. The proof of concept solution employs association rules as resulting patterns, the evaluation of which is to be automated. Association rules are mined using the 4ft-Miner procedure of the LISp-Miner system. The proposed artifacts have been evaluated through methods of Experiments, Scenarios, Functional testing, Dynamic analysis and Comparison. The evaluation methods indicate that the External Knowledge Framework can be used to automate the evaluation phase of descriptive data mining task in the financial services industry domain. Furthermore, the integration of External Knowledge Framework and FOFRADAR has been proposed. Způsob přístupu: Internet aplikovaná informatika [obor disert. práce] disertace fd132024 czenas dissertations eczenas data mining descriptive data mining automation evaluation phase of data mining task external data Rauch, Jan, 1948- jn20001227021 ths Kliegr, Tomáš ISIS:8484 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/43588/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/43588 Hlavní práce https://insis.vse.cz/zp/43588/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/43588/posudek/oponent/60510 Oponentura https://insis.vse.cz/zp/43588/posudek/oponent/60528 Oponentura https://insis.vse.cz/zp/43588/posudek/oponent/60529 Oponentura https://insis.vse.cz/zp/43588/podrobnosti dc:identifier NEPOSILAT VSKP vse43588 190531 43588