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Neural Networks and Rough Sets
Čurilla, Matej ; Hrubý, Martin (referee) ; Zbořil, František (advisor)
Rough sets and neural networks both offer good theoretical background for data processing and analysis. However, both of them suffer from few issues. This thesis will investigate methods by which these two concepts are merged, and few such solutions will be implemented and compared with conventional algorithm to study the benefits of this approach.
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Data Mining with Python
Šenovský, Jakub ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
The main goal of this thesis was to get acquainted with the phases of data mining, with the support of the programming languages Python and R in the field of data mining and demonstration of their use in two case studies. The comparison of these languages in the field of data mining is also included. The data preprocessing phase and the mining algorithms for classification, prediction and clustering are described here. There are illustrated the most significant libraries for Python and R. In the first case study, work with time series was demonstrated using the ARIMA model and Neural Networks with precision verification using a Mean Square Error. In the second case study, the results of football matches are classificated using the K - Nearest Neighbors, Bayes Classifier, Random Forest and Logical Regression. The precision of the classification is displayed using Accuracy Score and Confusion Matrix. The work is concluded with the evaluation of the achived results and suggestions for the future improvement of the individual models.
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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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Functionality Extension of Data Mining System on NetBeans Platform
Šebek, Michal ; Zendulka, Jaroslav (referee) ; Lukáš, Roman (advisor)
Databases increase by new data continually. A process called Knowledge Discovery in Databases has been defined for analyzing these data and new complex systems has been developed for its support. Developing of one of this systems is described in this thesis. Main goal is to analyse the actual state of implementation of this system which is based on the Java NetBeans Platform and the Oracle database system and to extend it by data preprocessing algorithms and the source data analysis. Implementation of data preprocessing components and changes in kernel of this system are described in detail in this thesis.
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Data Mining for Suggesting Further Actions
Veselovský, Martin ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
Knowledge discovery from databases is a complex issue involving integration, data preparation, data mining using machine learning methods and visualization of results. The thesis deals with the whole process of knowledge discovery, especially with the issue of data warehousing, where it offers the design and implementation of a specific data warehouse for the company ROI Hunter, a.s. In the field of data mining, the work focuses on the classification and forecasting of the advertising data available from the prepared data warehouse and, in particular, on the decision tree classification. When predicting the development of new ads, emphasis is put on the rationale for the prediction as well as the proposal to adjust the ad settings so that the prediction ends positively and, with a certain likelihood, the ads actually get better results.
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Analysis of Mobile Devices Network Communication Data
Abraham, Lukáš ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
At the beginning, the work describes DNS and SSL/TLS protocols, it mainly deals with communication between devices using these protocols. Then we'll talk about data preprocessing and data cleaning. Furthermore, the thesis deals with basic data mining techniques such as data classification, association rules, information retrieval, regression analysis and cluster analysis. The next chapter we can read something about how to identify mobile devices on the network. We will evaluate data sets that contain collected data from communication between the above mentioned protocols, which will be used in the practical part. After that, we finally get to the design of a system for analyzing network communication data. We will describe the libraries, which we used and the entire system implementation. We will perform a large number of experiments, which we will finally evaluate.
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Data Preprocessing
Vašíček, Radek ; Beran, Jan (referee) ; Honzík, Petr (advisor)
This thesis surveys on problems preprocessing data. Forepart deal with view and description characteristic tests for description attributes, methods for work with data and attributes. Second part work describes work with program Rapidminer. It pays pay attention to single functions preprocessing in this programme describes their function. Third part equate to results with using methods preprocessing and without using data preprocessing.
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Classification of Music Files Using Machine Learning
Sládek, Matyáš ; Smrčka, Aleš (referee) ; Janoušek, Vladimír (advisor)
This thesis is focused on classification of music files using machine learning algorithms. Seven classifiers were compared in this thesis, based on classification accuracy and speed. Two feature extraction methods, two feature selection methods and two parameter optimization methods were used. The best classifier proved to be XGBClassifier, which had reached accuracy of 87.56 % on dataset Extended Ballroom Dataset, 64.56 % on dataset FMA: A Dataset For Music Analysis and 83.50 % on dataset GTZAN. This model could be used for playlist creation or music database categorization.
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Data Mining
Slezák, Milan ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
The thesis is focused on an introduction of data mining. Data mining is focused on finding of a hidden data correlation. Interest in this area is dated back to the 60th the 20th century. Data analysis was first used in marketing. However, later it expanded to more areas, and some of its options are still unused. One of methodologies is useful used for creating of this process. Methodology offers a concise guide on how you can create a data mining procedure. The data mining analysis contains a wide range of algorithms for data modification. The interest in data mining causes that number of data mining software is increasing. This thesis contains overviews some of this programs, some examples and assessment.
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Toolbox for automatic EEG data quality assessment
Meloun, Jan ; Gajdoš, Martin (referee) ; Lamoš, Martin (advisor)
This thesis deals with the design of a tool for the automatic evaluation of EEG data quality. The theoretical part of the thesis contains a description of the formation and propagation of the action potential through the nervous system. Furthermore, a theoretical description of the EEG recording and its artifacts. The following is a description of the methods used to detect artifacts. In the practical part of the thesis, there is a description of the design of the tool for automatic EEG quality assessment, including a discussion of the results based on the provided data.
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