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Implementation of Mining Modules of Data Mining System on NetBeans Platform
Stríž, Rostislav ; Bartík, Vladimír (referee) ; Šebek, Michal (advisor)
Data collecting plays an important role in many aspects of today's businesses and quality information is the key to success. Process called Knowledge Discovery in Databases makes possible to extract hidden information that can be used further in our efforts. Main goal of this thesis is to describe an addition to such Data Mining System. Main objective is to create data mining module for NetBeans application, developed for demonstrational purposes by Faculty of Information Technology. New module is going to be able to mine information from Oracle database server via unusual use of Genetic Algorithm. This thesis describes the whole process of module implementation, begining with theoretical basics through coding details to final testing and summary.
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Mining Multiple Level Association Rules
Nguyenová, Thanh Lam ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with multiple level association rules mining. The aim of this work is to focus on available algorithms for mining multiple level association rules and to implement an application with a graphical user interface that will demonstrate the functionality of these algorithms. Five algorithms based on the Apriori algorithm were chosen. Experiments with each algorithm were performed using the application and the results were compared and evaluated at the end of the thesis.
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Mining Multi-Level Sequential Patterns
Šebek, Michal ; Platoš, Jan (referee) ; Popelínský, Lubomír (referee) ; Zendulka, Jaroslav (advisor)
Dolování sekvenčních vzorů je důležitá oblast získávání znalostí z databází. Stále více průmyslových a obchodních aplikací uchovává data mající povahu sekvencí, kdy je dáno pořadí jednotlivých transakcí. Toho může být využito například při analýze po sobě jdoucích nákupů zákazníků. Tato práce se zabývá využitím hierarchického uspořádání položek při dolování sekvenčních vzorů. V rámci práce jsou řešeny dvě základní oblasti - dolování víceúrovňových sekvenčních vzorů s křížením a bez křížení úrovní hierarchií. Dolovací úlohy pro obě oblasti jsou v práci formalizovány a následně navrženy algoritmy hGSP a MLSP pro jejich řešení. Experimentálně bylo ověřeno, že především algoritmus MLSP dosahuje výborných výkonnostních vlastností a stability. Význam nově získaných vzorů je ukázán na dolování reálných produkčních dat.
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Methods for Clustering Data
Pohlídal, Antonín ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This bachelor's thesis deals with hierarchical clustering methods with a focus on implementation of agglomerative hierarchical clustering method and its comparison with the DENCLUE method. First of all, various methods are described with emphasis on hierarchical clustering methods. Further, there is an implementation of the selected method, using the Java programming language and MySQL database. The last part contains a comparison with the implementation of DENCLUE method, implemented by Mr. Bc. Radim Kapavík.
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Knowledge Discovery in Multimedia Databases
Málik, Peter ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.
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Data Mining Module of a Data Mining System on NetBeans Platform
Výtvar, Jaromír ; Křivka, Zbyněk (referee) ; Zendulka, Jaroslav (advisor)
The aim of this work is to get basic overview about the process of obtaining knowledge from databases - datamining and to analyze the datamining system developed at FIT BUT on the NetBeans platform in order to create a new mining module. We decided to implement a module for mining outliers and to extend existing regression module with multiple linear regression using generalized linear models. New methods using existing methods of Oracle Data Mining.
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Knowledge Discovery in Image Databases
Jaroš, Ondřej ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This thesis is focused on knowledge discovery from databases, especially on methods of classification and prediction. These methods are described in detail. Furthermore, this work deals with multimedia databases and the way these databases store data. In particular, the method for processing low-level image and video data is described. The practical part of the thesis focuses on the implementation of this GMM method used for extracting low-level features of video data and images. In other parts, input data and tools, which the implemented method was compared with, are described. The last section focuses on experiments comparing extraction efficiency features of high-level attributes of low-level data and the methods implemented in selected classification tools LibSVM.
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Mining Modules of the Data Mining System in Oracle
Mader, Pavel ; Křivka, Zbyněk (referee) ; Zendulka, Jaroslav (advisor)
This master's thesis deals with questions of the data mining and an extension of a data mining system in the Oracle environment developed at FIT. So far, this system cannot apply to real-life conditions as there are no data mining modules available. This system's core application design includes an interface allowing the addition of mining modules. Until now, this interface has been tested on a sample mining module only; this module has not been executing any activity just demonstrating the use of this interface. The main focus of this thesis is the study of this interface and the implementation of a functional mining module testing the applicability of the implemented interface. Association rule mining module was selected for implementation.
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Success Rate Measure Methods in Data Mining
Trunkát, Jan ; Zelený, Jan (referee) ; Bartík, Vladimír (advisor)
The Bachelor thesis is aimed at success rate measure methods in data mining in the area of clustering. It introduces the basic concepts, features of data mining and especially the cluster analysis. This work includes program, which implements methods of measuring success. In conclusion, they are given results of clustering success.
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Mining Modules of Data Mining System on NetBeans Platform
Henkl, Tomáš ; Lukáš, Roman (referee) ; Zendulka, Jaroslav (advisor)
The master's thesis deals with the knowledge discover in databases and with the extending of the data mining systems in the Oracle environment developed at the VUT FIT. The system kernel conception incorporates an interface that enables the adding of data mining modules. The objective of the thesis is to learn this interface and implement and embed the data mining module for decision-tree classification into the application. In addition, the thesis compares the application with similar commercial product SAS Enterprise Miner
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