National Repository of Grey Literature 102 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Data Analysis of a Company Producing Medical Supplies
Kulhánková, Monika ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
This bachelor's thesis deals with the analysis of the company's sales data, specifically the classification of the customer's type according to his sales data. It provides a theoretical introduction to data mining. It describes the classification process and methods for creating classifiers and presents the CRISP-DM model. This thesis describes the provided data sets, from which the relevant attributes are selected. The data are preprocessed and used in the creation and testing of classification models. The result of this thesis is a comparison of the achieved results.
Analysis of Outlier Detection Methods
Labaš, Dominik ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
The topic of this thesis is analysis of methods for detection of outliers. Firstly, a description of outliers and various methods for their detection is provided. Then a description of selected data sets for testing of methods for detection of outliers is given. Next, an application design for the analysis of the described methods is presented. Then, technologies are presented, which provide models for described methods of detection of outliers. The implementation is then described in more detail. Subsequently, the results of experiments are presented, which represent the main part of this thesis. The results are evaluated and the individual models are compared with each other. Lastly, a method for accelerating outlier detection is demonstrated.
Application of Unsupervised Learning Methods in Graph Similarity Search
Sabo, Jozef ; Burgetová, Ivana (referee) ; Křivka, Zbyněk (advisor)
Goal of this master's thesis was in cooperation with the company Avast to design a system, which can extract knowledge from a database of graphs. Graphs, used for data mining, describe behaviour of computer systems and they are anonymously inserted into the company's database from systems of the company's products users. Each graph in the database can be assigned with one of two labels: clean or malware (malicious) graph. The task of the proposed self-learning system is to find clusters of graphs in the graph database, in which the classes of graphs do not mix. Graph clusters with only one class of graphs can be interpreted as different types of clean or malware graphs and they are a useful source of further analysis on the graphs. To evaluate the quality of the clusters, a custom metric, named as monochromaticity, was designed. The metric evaluates the quality of the clusters based on how much clean and malware graphs are mixed in the clusters. The best results of the metric were obtained when vector representations of graphs were created by a deep learning model (variational  graph autoencoder with two relation graph convolution operators) and the parameterless method MeanShift was used for clustering over vectors.
Methods for Mining Sequential Patterns
Fekete, Martin ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Sequential pattern mining is a field of data mining with wide applications. Currently, there are a number of algorithms and approaches to the problem of sequential pattern mining. The aim of this work is to design and implement an application designed for sequential pattern mining and use it to experimentally compare the chosen algorithms. Experiments are performed with both synthetic and real databases. The output of the work is a summary of the advantages and disadvantages of each algorithm for different kinds of input databases and an application implementing the selected algorithms of the SPMF library.
Use of Knowledge Discovery for Data from PDF Files
Dvořáček, Libor ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with the extraction of tables from digitally created pdfs and the subsequent use of the obtained data for data analysis. Methods of dimension reduction and cluster analysis are used. The main content is an analysis of available tools for data extraction in the python language, a description and comparison of the used machine learning methods and implementation of an application that combines all these topics into one functional unit at: http://extraktor.herokuapp.com
Statistical Analysis of Data from PDF Files
Oltmanová, Kristína ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This thesis is concerning the process of data extraction from tables from documents in PDF format and their subsequent analysis with the exploitation of statistical methods. The goal of this thesis is to demonstrate the process of obtaining, processing and analyzing data from PDF files, which, in consideration of their program processing, create a finite number of subgroups with common characteristics. Firstly, the reader will become acquainted with the fundamentals of PDF file processing and basic mathematical principles that are required in order to statistically evaluate given data. Obtained theoretical principles are then applied to practical use and programming form in the Python programming language. The resulting web application is programmed using the Flask Python library and is usable on a local server. 
Anomaly Detection in IEC 61850 Communication
Pešková, Daniela ; Burgetová, Ivana (referee) ; Matoušek, Petr (advisor)
This thesis deals with anomaly detection in industrial communication IEC 61850. It studies using various statistical analysis methods and probabilistic automata for creating communication profiles and its accuracy while detecting anomalies.
Mobile Application Identification Based on TLS Data
Borbély, Richard ; Matoušek, Petr (referee) ; Burgetová, Ivana (advisor)
This thesis deals with identification of mobile applications based on data from network protocol TLS. It conducts a research of values from the TLS handshake, specifically of JA3, JA3S and SNI values. The work represents an application that includes an algorithm performing a classification over TLS data. The results of the classification represent information based on which we can decide, if the identification of the apps was successful. This method allowed to identify 17 of the 18 given applications. The benefit of this work is the ability to identify mobile apps based on JA3, JA3S and SNI values and for example, it can be used in network administration.
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.
Evaluation of Betting Odds of Premier League's Matches
Zejda, Tomáš ; Burgetová, Ivana (referee) ; Hynek, Jiří (advisor)
Betting on sports is currently a trending phenomenon and this thesis is focusing on evaluating key factors for betting on the Premier League football matches. The aim of this thesis is to provide a better with an answer to whether it is a good idea to make a bet on a certain match considering current odds and risk factors or not. In this thesis we analysed certain amount of data on which the match results shall be depending on. We collected the data from previous seasons of this League as well as from other football competitions the Premier League teams take part in.

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