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Knowledge Discovery from Web Logs
Valaštín, Samuel ; Rychlý, Marek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.
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Similarity Searching in Network Data
Hud, Jakub ; Krobot, Pavel (referee) ; Wrona, Jan (advisor)
This bachelor thesis is interested in analyzing IP flow records. IP flow record contains IP flow metadata of specific network communication such as IP addresses, port numbers, network protocol numbers and other. Main goal is to design and implement metrices to determine similarity of NetFlow records. At the beginning of this thesis is description of how to analyze great amount of data. Next there are shown network monitoring technicies and NetFlow. Other parts of this thesis are dedicated to design and implementation of data analysis using DBSCAN algorithm. Implementation of data analysis application is also part of this thesis. As a result, the application can be used to network scan detection using NetFlow data although the results are not very clear and contain a lot of legitimate communication.
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Knowledge Discovery in Multimedia Databases
Málik, Peter ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.
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Memory Reduction of Stateful Network Traffic Processing
Hlaváček, Martin ; Puš, Viktor (referee) ; Kořenek, Jan (advisor)
This master thesis deals with the problems of memory reduction in the stateful network traffic processing. Its goal is to explore new possibilities of memory reduction during network processing. As an introduction this thesis provides motivation and reasons for need to search new method for the memory reduction. In the following part there are theoretical analyses of NetFlow technology and two basic methods which can in principle reduce memory demands of stateful processing. Later on, there is described the design and implementation of solution which contains the application of these two methods to NetFlow architecture. The final part of this work summarizes the main properties of this solution during interaction with real data.
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Similarity Searching in Network Data
Hud, Jakub ; Matoušek, Denis (referee) ; Wrona, Jan (advisor)
This bachelor thesis is interested in analyzing IP flow records. IP flow record contains IP flow metadata of specific network communication such as IP addresses, port numbers, network protocol numbers and other. Main goal is to design and implement method for determination of similarity of NetFlow records. At the beginning of this thesis is description of how to analyze great amount of data. Next there are shown network monitoring technicies and NetFlow. Other parts of this thesis are dedicated to design and implementation of data analysis using DBSCAN and agglomerative hierarchical clustering algorithms. Implementation of data analysis application is also part of this thesis. As a result, the application can be used to network scan detection using NetFlow data although the results are not very clear and contain a lot of legitimate communication.
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Computer Library with Clustering Methods
Riša, Martin ; Homoliak, Ivan (referee) ; Košík, Michal (advisor)
The aim of this work is to create a library with chosen clustering methods, to compare their effectiveness and their properties by testing them on different input data sets. The aim of the testing is to determine efficiency of a method, to determine advantages and disadvantages of a method to cluster general input data or to cluster only data of specific shapes. Stages of development of the library are also documented in the text of this work.
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Evaluation of the Cluster Analysis Quality
Schmid, Michael ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This bachelor's thesis concerns cluster analysis and possible ways to evaluate the quality of its results. The thesis contains theoretical introduction to cluster analysis and metrics used for evaluation of quality of its results. The thesis also documents development of an application capable of evaluating quality of results of cluster analysis using mentioned metrics. Important part of the thesis describes experiments conducted with implemented application, including design of the experiments and analysis of behavior of clustering algorithms and metrics when they are used in combination with various datasets.
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Analysis of Data on Social Networks Based on Data Mining
Fešar, Marek ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The thesis presents general principles of data mining and it also focuses on specific needs of social networks. Certain social networks, chosen with respect to popularity and availability to Czech users, are discussed from various points of view. The benefits and drawbacks of each are also mentioned. Afterwards, one suitable API is selected for futher analysis. The project explains harvesting data via Twitter API and the process of mining of data from this particular network. Design of a mining algorithm inspired by density based clustering methods is described. The implementation is explained in its own chapter, preceded by thorough explanation of MVC architectural pattern. In the end some examples of usage of gathered knowledge are shown as well as possibility of future extensions.
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System For Analysis of Biathlon Statistics
Zeman, Ondřej ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
The goal of this bachelor's thesis is to create a web application that downloads biathlon data and statistics and use the technique to get knowledge from data to get interesting and unusual information. In this work are solving descriptive and predictive mining tasks. Clustering algorithms have been used for descriptive mining tasks and for search patterns in course and shooting statistics. Prediction of race results is solved by using multiple linear regression. The application is implemented in Python. Web application is available at https://analysisofbiathlonstatistics.herokuapp.com.
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Autonomous Single-Channel Deinterleaving
Tomešová, Tereza ; Žák, Libor (referee) ; Hübnerová, Zuzana (advisor)
This thesis deals with an autonomous single-channel deinterleaving. An autonomous single-channel deinterleaving is a separation of the received sequence of impulses from more than one emitter to sequences of impulses from one emitter without a human assistance. Methods used for deinterleaving could be divided into single-parameter and multiple-parameter methods according to the number of parameters used for separation. This thesis primarily deals with multi-parameter methods. As appropriate methods for an autonomous single-channel deinterleaving DBSCAN and variational bayes methods were chosen. Selected methods were adjusted for deinterleaving and implemented in programming language Python. Their efficiency is examined on simulated and real data.
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