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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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Implementation of data preparation procedures for RapidMiner
Černý, Ján ; Berka, Petr (advisor) ; Kliegr, Tomáš (referee)
Knowledge Discovery in Databases (KDD) is gaining importance with the rising amount of data being collected lately, despite this analytic software systems often provide only the basic and most used procedures and algorithms. The aim of this thesis is to extend RapidMiner, one of the most frequently used systems, with some new procedures for data preprocessing. To understand and develop the procedures, it is important to be acquainted with the KDD, with emphasis on the data preparation phase. It's also important to describe the analytical procedures themselves. To be able to develop an extention for Rapidminer, its needed to get acquainted with the process of creating the extention and the tools that are used. Finally, the resulting extension is introduced and tested.
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Data mining applications in business practice
Trávníček, Petr ; Pour, Jan (advisor) ; Svatoš, Oleg (referee)
Throughout last decades, knowledge discovery from databases as one of the information and communicaiton technologies' disciplines has developed into its current state being showed increasing interest not only by major business corporates. Presented diploma thesis deals with problematique of data mining while paying prime attention to its practical utilization within business environment. Thesis objective is to review possibilities of data mining applications and to decompose implementation techniques focusing on specific data mining methods and algorithms as well as adaptation of business processes. This objective is subject of theoretical part of thesis focusing on principles of data mining, knowledge discovery from databases process, data mining commonly used methods and algorithms and finally tasks typically implemented in this domain. Further objective consists in presenting data mining benefits on the model example that is being displayed in the practical part of the thesis. Besides created data mining models evalution, practical part contains also design of subsequent steps that would enable higher efficiency in some specific areas of given business. I believe previous point together with characterization of knowledge discovery in databases process to be considered as the most beneficial one's of the thesis.
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Generating data using the LM Reverse-Miner
Stluka, Jakub ; Šimůnek, Milan (advisor) ; Kliegr, Tomáš (referee)
In past years, great attention has been paid to evolutionary algorithms and they have been utilized in wide range of industries including data mining field, which nowadays presents a highly demanded product for many commercial institutions. Both mentioned topics are combined in this work. Main thesis subject is testing of new Reverse-Miner module, which can generate data with hidden properties using evolutionary algorithms while using also other modules of LISp-Miner system, commonly used for the purposes of data mining. Main goal lies in generation of two databases by the module in such way so they would meet explicitly set requirements. Other goals are also set within the thesis in the form of understanding the domain necessary for subsequent modeling. The result of the practical part of the thesis is represented not only by two successfully generated databases, but also by description of steps, methods and techniques used. The common recommendations for data preparation by module Reverse-Miner are later summarized, based on experience with modeling. Previous thesis outputs are furthermore contemplating the conclusion of analysis of technical means used for generation and they also provide several suggestions for possible future extensions.
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Zlepšování učinnosti prevence v telemedicíně
Nálevka, Petr ; Svátek, Vojtěch (advisor) ; Berka, Petr (referee) ; Štěpánková, Olga (referee) ; Šárek, Milan (referee)
This thesis employs data-mining techniques and modern information and communication technology to develop methods which may improve efficiency of prevention oriented telemedical programs. In particular this thesis uses the ITAREPS program as a case study and demonstrates that an extension of the program based on the proposed methods may significantly improve the program's efficiency. ITAREPS itself is a state of the art telemedical program operating since 2006. It has been deployed in 8 different countries around the world, and solely in the Czech republic it helped prevent schizophrenic relapse in over 400 participating patients. Outcomes of this thesis are widely applicable not just to schizophrenic patients but also to other psychotic or non-psychotic diseases which follow a relapsing path and satisfy certain preconditions defined in this thesis. Two main areas of improvement are proposed. First, this thesis studies various temporal data-mining methods to improve relapse prediction efficiency based on diagnostic data history. Second, latest telecommunication technologies are used in order to improve quality of the gathered diagnostic data directly at the source.
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Practical applications of data mining technologies in health insurance companies
Kulhavý, Lukáš ; Pour, Jan (advisor) ; Kučera, Petr (referee)
This thesis focuses on data mining technology and its possible practical use in the field of health insurance companies. Thesis defines the term data mining and its relation to the term knowledge discovery in databases. The term data mining is explained, inter alia, with methods describing the individual phases of the process of knowledge discovery in databases (CRISP-DM, SEMMA). There is also information about possible practical applications, technologies and products available in the market (both products available free and commercial products). Introduction of the main data mining methods and specific algorithms (decision trees, association rules, neural networks and other methods) serves as a theoretical introduction, on which are the practical applications of real data in real health insurance companies build. These are applications seeking the causes of increased remittances and churn prediction. I have solved these applications in freely-available systems Weka and LISP-Miner. The objective is to introduce and to prove data mining capabilities over this type of data and to prove capabilities of Weka and LISP-Miner systems in solving tasks due to the methodology CRISP-DM. The last part of thesis is devoted the fields of cloud and grid computing in conjunction with data mining. It offers an insight into possibilities of these technologies and their benefits to the technology of data mining. Possibilities of cloud computing are presented on the Amazon EC2 system, grid computing can be used in Weka Experimenter interface.
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