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Methods of Document Summarization on the Web
Belica, Michal ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
The work deals with automatic summarization of documents in HTML format. As a language of web documents, Czech language has been chosen. The project is focused on algorithms of text summarization. The work also includes document preprocessing for summarization and conversion of text into representation suitable for summarization algorithms. General text mining is also briefly discussed but the project is mainly focused on the automatic document summarization. Two simple summarization algorithms are introduced. Then, the main attention is paid to an advanced algorithm that uses latent semantic analysis. Result of the work is a design and implementation of summarization module for Python language. Final part of the work contains evaluation of summaries generated by implemented summarization methods and their subjective comparison of the author.
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Data Independency of the FIT-Miner Data Mining System
Novák, Ondřej ; Šebek, Michal (referee) ; Zendulka, Jaroslav (advisor)
System for data mining Fit-Miner is now dependant on only one specific DBMS. This master’s thesis deals with analysis of implementation that works with database, modules and functions for data mining. Next it shows the set of changes which will allow FIT-Miner to work with another DBMS. And finally, a description of the implementation of these changes.
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Creation of Unit for Datamining
Krásenský, David ; Burgetová, Ivana (referee) ; Lukáš, Roman (advisor)
The goal of this work is to create data mining module for information system Belinda. Data from database of clients will be analyzed using SAS Enterprise Miner. Results acquired using several data mining methods will be compared. During the second phase selected data mining method will be implemented such as module of information system Belinda. The final part of this work is evaluation of acquired results and possibility of using this module.
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Comparation of Models for Datamining
Pospíšil, Jan ; Bartík, Vladimír (referee) ; Lukáš, Roman (advisor)
This thesis focuses on comparing of the datamining models features depending on the different databazis topology. The objekt was to find key features that at most involve the accuracy of classification. Thesis is composed from chapters in a way that even a non-professional or even a complete laik could understand the object and could find theese thesis results useful. In the beginning the reader is beeing made familiar with all the background information about datamining and its models and algorithms, the second part denotes about the model comparison and discusses its results.
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Classification in Data Streams Using Ensemble Methods
Jarosch, Martin ; Zendulka, Jaroslav (referee) ; Hlosta, Martin (advisor)
This master's thesis deals with knowledge discovery and is focused on data stream classification. Three ensemble classification methods are described here. These methods are implemented in practical part of this thesis and are included in the classification system. Extensive measurements and experimentation were used for method analysis and comparison. Implemented methods were then integrated into Malware analysis system. At the conclusion are presented obtained results.
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Web Application of Recommender System
Hlaváček, Pavel ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with problems of recommender systems and their usage in web applications. There are three main data mining techniques summarized and individual approaches for recommendation. Main part of this thesis is a suggestion and an implementation of web applications for recommending dishes from restaurants. Algorithm for food recommending is designed and implemented in this paper. The algorithm deals with the problem of frequently changing items. The algorithm utilizes hybrid filtering technique which is based on content and knowledge. This filtering technique uses cosine vector similarity for computation.
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Extension of Behavioral Analysis of Network Traffic Focusing on Attack Detection
Teknős, Martin ; Zbořil, František (referee) ; Homoliak, Ivan (advisor)
This thesis is focused on network behavior analysis (NBA) designed to detect network attacks. The goal of the thesis is to increase detection accuracy of obfuscated network attacks. Methods and techniques used to detect network attacks and network traffic classification were presented first. Intrusion detection systems (IDS) in terms of their functionality and possible attacks on them are described next. This work also describes principles of selected attacks against IDS. Further, obfuscation methods which can be used to overcome NBA are suggested. The tool for automatic exploitation, attack obfuscation and collection of this network communication was designed and implemented. This tool was used for execution of network attacks. A dataset for experiments was obtained from collected network communications. Finally, achieved results emphasized requirement of training NBA models by obfuscated malicious network traffic.
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Porovnání nekomerčních nástrojů pro dolování znalosti z dat pomocí strojového učení
Ondrejka, Petr
This diploma thesis concerns with features and abilities of chosen software tools for data mining. An important part is the selection and evaluation of each criteria by which the comparison of tools is done. The basic issues of data mining and machine learning are determined here. The comparison criteria are chosen with consideration of the necessary operations that are required to realize the whole process of data mining. The criteria are evaluated on the same level of importance, without weighting. The most widely used algorithms for data mining are chosen for the comparison of time and memory demandingness. The result is the description of selected tools based on the comparison criteria and evaluation of these criteria. The comparison results must be understood with respect of testing data, chosen hardware, and the fact that no weighting was used.
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