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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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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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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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Association Rules Mining over Data Warehouses
Hlavička, Ladislav ; Chmelař, Petr (referee) ; Stryka, Lukáš (advisor)
This thesis deals with association rules mining over data warehouses. In the first part the reader will be familiarized with terms like knowledge discovery in databases and data mining. The following part of the work deals with data warehouses. Further the association analysis, the association rules, their types and mining possibilities are described. The architecture of Microsoft SQL Server and its tools for working with data warehouses are presented. The rest of the thesis includes description and analysis of the Star-miner algorithm, design, implementation and testing of the application.
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Creation of New Clasification Units in Data Mining System on NetBeans Platform
Kmoščák, Ondřej ; Bartík, Vladimír (referee) ; Lukáš, Roman (advisor)
This diploma thesis deals with the data mining and the creation of data mining unit for data mining system, which is beeing developed at FIT. This is a client application consisting of a kernel and its graphical user interface and independent mining modules. The application uses support of Oracle Data Mining. The data mining system is implemented in Java language and its graphical user interface is built on NetBeans platform. The content of this work will be the introduction into the issue of knowledge discovery and then the presentation of the chosen Bayesian classification method, for which there will subsequently be implemented the stand-alone data mining module. Furthermore, the implementation of this module will be described.
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Sequential Pattern Mining
Tisoň, Zdeněk ; Zendulka, Jaroslav (referee) ; Hlosta, Martin (advisor)
This master's thesis is focused on knowledge discovery from databases, especially on methods of mining sequential patterns. Individual methods of mining sequential patterns are described in detail. Further, this work deals with extending the platform Microsoft SQL Server Analysis Services of new mining algorithms. In the practical part of this thesis, plugins for mining sequential patterns are implemented into MS SQL Server. In the last part, these algorithms are compared on different data sets.
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Data Mining Case Study in Python
Stoika, Anastasiia ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis focuses on basic concepts and techniques of the process known as knowledge discovery from data. The goal is to demonstrate available resources in Python, which enable to perform the steps of this process. The thesis addresses several methods and techniques focused on detection of unusual observations, based on clustering and classification. It discusses data mining task for data with the limited amount of inspection resources. This inspection activity should be used to detect unusual transactions of sales of some company that may indicate fraud attempts by some of its salespeople.
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