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Comparation of Models for Datamining
Gabriš, Ondrej ; Stryka, Lukáš (referee) ; Burgetová, Ivana (advisor)
Increasing development of information technology causes the amount of produced data to grow continously. And so the need becomes more intensive to process the produced data fast and efficiently to discover hidden knowledge contained in the data. This thesis examines the process of knowledge discovery in data, it's particular phases, various methods for mining the data and their comparation. Models of regression, neural network and decision tree are analysed in detail. The thesis also introduces one of the leading tools for datamining the SAS Enterprise Miner and demonstrates it's practical application on data. The purpose of this thesis is comparation of models for datamining in the SAS Enterprise Miner environment, discussion of the results and analysis to determine which model is suitable for different kinds of mined data.
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Multi-Level Association Rules Module of a Data Mining System
Pospíšil, Jan ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis focuses on the problematics of implementing a multilevel association rules mining module, for existing data mining project. There are two main algorithms explained, Apriori and MLT2L1. The thesis continues with the datamining module implementation, as well as the DMSL elements design. In the final chapters deal with an example dataminig task and its result comparison as well as the whole thesis achievement description.
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Web Page Classification
Kolář, Roman ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This paper presents problem of automatic webpages classification using association rules based classifier. Classification problem is presented, as a one of datamining technique, in context of mining knowledges from text data. There are many text document classification methods presented with highlighting benefits of classification methods using association rules. The main goal of work is adjusting selected classification method for relation data and design draft of webpages classifier, which classifies pages with the aid of visual properties - independent section layout on the web page, not (only) by textual data. There is also ARC-BC classification method presented as a selected method and as one of intriguing classificators, that derives accuracy and understandableness benefits of all other methods.
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System for Testing of Business Strategy
Lanc, Martin ; Květoňová, Šárka (referee) ; Bartík, Vladimír (advisor)
Aim of this thesis is to introduce questions about trading stocks on global stock exchange. It shows up basics ideas, which are necessary to understand the system of trading stocks, building a bussines strategy and its automatization by simple information technology techniques. In the following, there is a description of concept and implementation of business system for testing a trading strategy, which is based on historical market data analysis. The next part of this work is focused on the demonstration system and its expansion possibilities. Whole aplication is created by means of scripting language PHP and Javascript, markup language HTML, using the MySQL database system.
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Comparation of Models for Datamining
Pospíšil, Jan ; Bartík, Vladimír (referee) ; Lukáš, Roman (advisor)
This thesis focuses on comparing of the datamining models features depending on the different databazis topology. The objekt was to find key features that at most involve the accuracy of classification. Thesis is composed from chapters in a way that even a non-professional or even a complete laik could understand the object and could find theese thesis results useful. In the beginning the reader is beeing made familiar with all the background information about datamining and its models and algorithms, the second part denotes about the model comparison and discusses its results.
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Creation of Database Application and Solutions for Business Intelligence
Městka, Milan ; Kott, Josef (referee) ; Kříž, Jiří (advisor)
Theme of this master’s thesis is design of software support for business intelligence. Design is realized in cooperation with corporation ZZN Pelhřimov a.s. Introduction is focused on theoretical description of business intelligence and datamining and also on development environment in which is project designed. Corporation is characterised also in introduction. Main part contains data collecting and definition of individual modules. In conclusion of this thesis will be several types of analysis from collected data and then according to these analysis, we can draw measures to improve current state of corporation.
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Datamining in data from financial institution
Fedorko, Michal ; Rauch, Jan (advisor) ; Kotlář, Ondřej (referee)
The main purpose of this paper is to create datamining analysys of volutary termations in financial institution based in Czech republic on the data stored by HR department. Only hard data currently stored was input. For creating analysys the CRISP-DM metodology was used. For modeling itself the LISp-Miner was used. Association rules were the main approach to solving the task..Several interesting association rules were found and interprated. Outcome of the paper is for internal campains of the customer and there is even motivation for furthure predictive modeling and this was the first step.
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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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Mapping of PMML and BKEF documents using PHP in the SEWEBAR CMS
Vojíř, Stanislav ; Kliegr, Tomáš (advisor) ; Zamazal, Ondřej (referee)
In the data mining process, it is necessary to prepare the source dataset - for example, to select the cutting or grouping of continuous data attributes etc. and use the knowledge from the problem area. Such a preparation process can be guided by background (domain) knowledge obtained from experts. In the SEWEBAR project, we collect the knowledge from experts in a rich XML-based representation language, called BKEF, using a dedicated editor, and save into the database of our custom-tailored (Joomla!-based) CMS system. Data mining tools are then able to generate, from this dataset, mining models represented in the standardized PMML format. It is then necessary to map a particular column (attribute) from the dataset (in PMML) to a relevant 'metaattribute' of the BKEF representation. This specific type of schema mapping problem is addressed in my thesis in terms of algorithms for automatic suggestion of mapping of columns to metaattributes and from values of these columns to BKEF 'metafields'. Manual corrections of this mapping by the user are also supported. The implementation is based on the PHP language and then it was tested on datasets with information about courses taught in 5 universities in the U.S.A. from Illinois Semantic Integration Archive. On this datasets, the auto-mapping suggestion process archieved the precision about 70% and recall about 77% on unknown columns, but when mapping the previously user-mapped data (using implemented learning module), the recall is between 90% and 100%.
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