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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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Clustering objects with the MCluster-Miner procedure of the LISp-Miner system
Pelc, Tomáš ; Šimůnek, Milan (advisor) ; Šulc, Zdeněk (referee)
This bachelor thesis deals with clustering objects with the MCluster-Miner procedure of the LISp-Miner system. The first aim of this bachelor thesis is clustering objects with the mentioned pro-cedure and analyzing its possible usage on different datasets. To achieve this goal, the procedure was applied on six different datasets. The secong aim of this thesis is to analyze and compare implemented algorithms, similarity measures and to propose recommendations for clustering parameters. To achieve this goal, the available algorithms and similarity measures are compared based on achieved results (the quality of distribution objects into clusters, the time of clustering task, the number of attributes used for clustering). Based on these comparisons, the recommen-dations for clustering parameters are proposed. The benefits of this thesis are these recommenda-tions, comparisons of available algorithms and similarity measures, summary of actual state (da-ted to May 2017) of the MCluster-Miner module and showing the possibility of displaying results of clustering task at the interactive analysis of geodata. The theoretical part comprises the description of the LISp-Miner system, basic clustering principles, clustering methods and similari-ty measures used by the GUHA-procedure MCluster-Miner, and the MCluster-Miner module. In the practical part the MCluster-Miner procedure is being applied on six different datasets and the achieved results are summarized there.
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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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Creation of web-based analytics report from LISp-Miner metabase analytics
Nepomucký, Pavel ; Rauch, Jan (advisor)
This diploma thesis deals with ways how to represent results of LISp-Miner application on the world wide web. This thesis has three main sections. The first section is dedicated to description of data analysis process including description of newly established study of infography and its application in publishing results found du-ring the DZD process. The second part describes exporting of LISp-Miner as well as exporting formats of each module and its combining with other technologies, afterwards follows summarization of all kind of exports of lispminer and its im-provements or create a whole new templates. Third part is dedicated to develop-ment of a web-based application as a tool of repsentation results generated by lispminer. The very last part is contained of future improvements of this application.
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Comparison of Approaches to Synthetic Data Generation
Šejvlová, Ludmila ; Šimůnek, Milan (advisor) ; Pavlíčková, Jarmila (referee)
The diploma thesis deals with synthetic data, selected approaches to their generation together with a practical task of data generation. The goal of the thesis is to describe the selected approaches to data generation, capture their key advantages and disadvantages and compare the individual approaches to each other. The practical part of the thesis describes generation of synthetic data for teaching knowledge discovery using databases. The thesis includes a basic description of synthetic data and thoroughly explains the process of their generation. The approaches selected for further examination are random data generation, the statistical approach, data generation languages and the ReverseMiner tool. The thesis also describes the practical usage of synthetic data and the suitability of each approach for certain purposes. Within this thesis, educational data Hotel SD were created using the ReverseMiner tool. The data contain relations discoverable with SD (set-difference) GUHA-procedures.
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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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Business rule learning using data mining of GUHA association rules
Vojíř, Stanislav ; Strossa, Petr (advisor) ; Pour, Jan (referee) ; Kouba, Zdeněk (referee) ; Gregor, Jiří (referee)
In the currently highly competitive environment, the information systems of the businesses should not only effectively support the existing business processes, but also have to be dynamically adaptable to the changes in the environment. There are increasing efforts at separation of the application and the business logic in the information system. One of the appropriate instruments for this separation is the business rule approach. Business rules are simple, understandable rules. They can be used for the knowledge externalization and sharing also as for the active control and decisions within the business processes. Although the business rule approach is used for almost 20 years, the various specifications and practical applications of business rules are still a goal of the active research. The disadvantage of the business rule approach is great demands on obtaining of the rules. There has to be a domain expert, who is able to manually write them. One of the problems addressed by the current research is the possibility of (semi)automatic acquisition of business rules from the different resources - unstructured documents, historical data etc. This dissertation thesis addresses the problem of acquisition (learning) of business rules from the historical data of the company. The main objective of this thesis is to design and validate a method for (semi)automatic learning of business rules using the data mining of association rules. Association rule are a known data mining method for discovering of interesting relations hidden in the data. Association rules are comprehensible and explainable. The comprehensibility of association rules is suitable for the use of them for learning of business rules. For this purpose the user can use not only simple rules discovered using the algorithm Apriori or FP-Growth, but also more complex association rules discovered using the GUHA method. Within this thesis is used the procedure 4ft-Miner implemented in the data mining system LISp Miner. The first part of this thesis contains the description of the relevant topics from the research of business rules and association rules. Business rules is not a name of one specification of standard but rather a label of the approach to modelling of business logic. As part of the work there is defined a process of selection of the most appropriate specification of business rules for the selected practical use. Consequently, the author proposed three models of involving of data mining of association rules into business rule sets. These models contain also the definition of a model for the transformation of GUHA association rules in the business rules for the system JBoss Drools. For the possibility of learning of business rules using the data mining results from more than one data set, the author proposed a knowledge base. The knowledge base is suitable for the interconnection of business rules and data mining of association rules. From the perspective of business rules the knowledge base is a term dictionary. From the perspective of data mining the knowledge base contains some background knowledge for data preprocessing and preparation of classification models. The proposed models have been validated using practical implementations in the systems EasyMiner (in conjunction with JBoss Drools) and Erian. The thesis contains also a description of two model use cases based on real data from the field of marketing and the field of health insurance.
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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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Datamining on publicly accessible data
Pangrác, Jiří ; Rauch, Jan (advisor) ; Chudán, David (referee)
This bachelor thesis deals with the datamining methods on publicly accessible data. Data mining is a technique of mining potentially interesting relations from data. Analysis is carried out on data provided by Česká obchodní inspekce, the czech office for trade inspection, which are accessible to public. I am trying to find possible answers for some analytical questions asked. For the analysis itself, LISp-Miner system was used focusing on 4ft-Miner and CF-Miner procedures. Besides the actual analysis, this thesis includes a brief description of LISp-Miner system and datamining generally. The main goal of this work is presentation of the results for their possible practical use.
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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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