National Repository of Grey Literature 306 records found  1 - 10nextend  jump to record: Search took 0.01 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.
Complex On-Line Training Diary
Kamenský, Zdeněk ; Hynek, Jiří (referee) ; Bartík, Vladimír (advisor)
Design and implementation of online training diary for athletes is the main goal of this thesis. At the beginning, it was necessary to explain some of key words, related to the thesis topic. One of the most important things is data mining and its usability for sports data analysis. After that, existing solutions of sport applications were analyzed and also it was obligatory to analyze potential users requirements. Application design, implementation and testing were the next steps. Some of data mining methods were used for analysis of sports data intended for individual athletes and their coaches.
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 Predictive Maintenance Algorithms for State Monitoring of an Experimental Pneumatic Device
Štastný, Petr ; Brablc, Martin (referee) ; Dobossy, Barnabás (advisor)
This bachelor thesis deals with finding state indicators of pneumatic cylinder using algorithms of machine learning and data mining. The goal was to determine measurable quantity and algorithm of its evaluating, using which would be possible to identify state and sources of failures. The data of behavior of pneumatic cylinder were acquished on testing stand, which was equipped by sensors of 16 different quantities. Postprocessing and evaluating of the data took place in Matlab tools, particularly Diagnostic Feature Designer and Classification Learner.
Cluster analysis in mathematical software
Starý, Josef ; Karpíšek, Zdeněk (referee) ; Žák, Libor (advisor)
This bachelor thesis is focused on methods of cluster analysis in mathematical software. The goal is to describe basic methods of cluster analysis, to describe their implementation in mathematical software, to use the methods for clustering of prepared data and to compare the functionality of chosen software.
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.
RQA System Anomaly Detection
Lorenc, Jan ; Jeřábek, Kamil (referee) ; Pluskal, Jan (advisor)
The aim of the theses is to design and implement a machine learning model for anomaly detection in Y Soft's RQA system. Owing to the microservice architecture, an anomaly is considered to be a recurring occurrence of outliers in durations of service requests or a considerable variance in error rate. The thesis outlines the current data collection process in the system and defines what kind of data describe the state of the system. It devises a suitable format of data storage for its subsequent analysis. It presents algorithms commonly used to solve anomaly detection problems. The anomaly detection is designed and implemented using cluster analysis and statistical methods. Finally, the thesis evaluates the quality of the detection and the achieved results.
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
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.
Implementation of a competitive intelligence process to a selected company
Václavková, Eliška ; Petráková, Anna (referee) ; Bartes, František (advisor)
This diploma Thesis deals with the introducing of the Comptitive Intelligence process and creating a proposal for the implementation of this process to the selected company. The first part contains a description of the theoretical framework, followed by the review of the current situation in the company and the reasons why the Competitive Intelligence process should be implemented. The next part is focused on the proposal of the whole process. The conclusion of the thesis sumarizes the whole proposal and its benefits of this thesis for the company.

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