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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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Knowledge Discovery in Multimedia Databases
Jurčák, Petr ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This master's thesis is dedicated to theme of knowledge discovery in Multimedia Databases, especially basic methods of classification and prediction used for data mining. The other part described about extraction of low level features from video data and images and summarizes information about content-based search in multimedia content and indexing this type of data. Final part is dedicated to implementation Gaussian mixtures model for classification and compare the final result with other method SVM.
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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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Association Rules Mining
Dvořák, Michal ; Chmelař, Petr (referee) ; Stryka, Lukáš (advisor)
The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.
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Data Mining Module
Petrlík, Jiří ; Fiala, Jiří (referee) ; Zendulka, Jaroslav (advisor)
The aim of the bachelor's theses is to introduce the problematic of data mining. I have especially focused on the problematic of the classification with the help of neural networks. This is why I describe some basic algorithms for neural network teaching as well. The main goal of this work is to create a new module for the system of data mining. This system has been developed in the cooperation with other people from FIT VUT in Brno. I introduce this system here as well and I also describe the proposal for it' s new module. I have already tested some training data with the final module.
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Knowledge Discovery in Spatio-Temporal Data
Pešek, Martin ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with knowledge discovery in spatio-temporal data, which is currently a rapidly evolving area of research in information technology. First, it describes the general principles of knowledge discovery, then, after a brief introduction to mining in the temporal and spatial data, it focuses on the overview and description of existing methods for mining in spatio-temporal data. It focuses, in particular, on moving objects data in the form of trajectories with an emphasis on the methods for trajectory outlier detection. The next part of the thesis deals with the process of implementation of the trajectory outlier detection algorithm called TOP-EYE. In order to testing, validation and possibility of using this algorithm is designed and implemented an application for trajectory outlier detection. The algorithm is experimentally evaluated on two different data sets.
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Intelligent Mailbox
Pohlídal, Antonín ; Drozd, Michal (referee) ; Chmelař, Petr (advisor)
This master's thesis deals with the use of text classification for sorting of incoming emails. First, there is described the Knowledge Discovery in Databases and there is also analyzed in detail the text classification with selected methods. Further, this thesis describes the email communication and SMTP, POP3 and IMAP protocols. The next part contains design of the system that classifies incoming emails and there are also described realated technologie ie Apache James Server, PostgreSQL and RapidMiner. Further, there is described the implementation of all necessary components. The last part contains an experiments with email server using Enron Dataset.
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Data Mining Module
Hlosta, Martin ; Stryka, Lukáš (referee) ; Zendulka, Jaroslav (advisor)
This thesis concerns knowledge discovery in databases (KDD), especially classification by Support Vector Machines (SVM). System for KDD has been developed at FIT BUT. For KDD process description is used language DMSL. The goal of the thesis was to extend DMSL with respect to SVM classifier, propose, implement and test a module for this system.
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Knowledge Discovery in Multimedia Databases
Jirmásek, Tomáš ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This master's thesis deals with knowledge discovery in databases, especially basic methods of classification and prediction used for data mining are described here. The next chapter contains introduction to multimedia databases and knowledge discovery in multimedia databases. The main goal of this chapter was to focus on extraction of low level features from video data and images. In the next parts of this work, there is described data set and results of experiments in applications RapidMiner, LibSVM and own developed application. The last chapter summarises results of used methods for high level feature extraction from low level description of data.
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