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Prediction of Values on a Time Line
Maršová, Eliška ; Bařina, David (referee) ; Zemčík, Pavel (advisor)
This work deals with the prediction of numerical series whose application is suitable for prediction of stock prices. They explain the procedures for analysis and works with price charts. Also explains the methods of machine learning. Knowledge is used to build a program that finds patterns in numerical series for estimation.
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Sentiment Analysis with Use of Data Mining
Sychra, Martin ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The theme of the work is sentiment analysis, especially in terms of informatics (marginally from a linguistic point of view). The linguistic part discusses the term sentiment and language methods for its analysis, e.g. lemmatization, POS tagging, using the list of stopwords etc. More attention is paid to the structure of the sentiment analyzer which is based on some of the machine learning methods (support vector machines, Naive Bayes and maximum entropy classification). On the basis of the theoretical background, a functional analyzer is projected and implemented. The experiments are focused mainly on comparing the classification methods and on the benefits of using the individual preprocessing methods. The success rate of the constructed classifier reaches up to 84 % in the cross-validation.
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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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Image Segmentation Using Height Maps
Moučka, Milan ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This thesis deals with image segmentation of volumetric medical data. It describes a well-known watershed technique that has received much attention in the field of medical image processing. An application for a direct segmentation of 3D data is proposed and further implemented by using ITK and VTK toolkits. Several kinds of pre-processing steps used before the watershed method are presented and evaluated. The obtained results are further compared against manually annotated datasets by means of the F-Measure and discussed.
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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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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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Spatial-temporal analysis of HD-EEG data in pacients with nerodegenerative disease
Jordánek, Tomáš ; Kozumplík, Jiří (referee) ; Lamoš, Martin (advisor)
This master’s thesis deals with diagnostics of prodromal stage of Lewy body disease using microstate analysis. First part of the thesis includes theoretical background which is needed for understanding discussed topics and presented results. This part consists of description of the disease, diagnostic options, electroencephalography, pre-processing of the EEG record and the microstate analysis process. Theoretical background is followed by a practical part of the thesis. In the beginning, there is a chapter about a dataset, used EEG device, and own solution of the pre-processing. Microstate analysis is discussed next, its output parameters were compared between groups with statistical methods. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections.
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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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