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Analysis of DDos data with clustering
Krátký, Matěj ; Šišmiš, Lukáš (referee) ; Setinský, Jiří (advisor)
This thesis focuses on the detection of distributed denial of service (DDoS) attacks using clustering algorithms. In the first part, different types of DDoS attacks and approaches to identify them are described. Next, the thesis studies clustering methods, specifically hierarchical and k-means clustering, for analyzing the network traffic associated with these attacks. It also includes the design of a detection system suitable for detecting DDoS attacks. This is followed by a description of the implementation of this system required for the analysis phase. The main part of the work consists of performing experiments on the available dataset and evaluating the effectiveness of the methods, parameters and attributes combinations used. Finally, the thesis discusses the application of the findings and the possibilities for further research in this area.
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Comparing of Clustering Algorithms
Jakšík, Aleš ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This bachelor thesis deals with the comparison of K-means, K-medoids and Fuzzy C-means algorithms. In the first part, it discusses the types of distance measurements, clustering evaluation options and categorization of clustering methods. In the second part, it presents a web application for comparing the selected methods and in the third part it discusses the comparison and evaluation of the results and statistics from running the clustering algorithms in the demonstration application.
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ECG Cluster Analysis
Pospíšil, David ; Kozumplík, Jiří (referee) ; Klimek, Martin (advisor)
This diploma thesis deals with the use of some methods of cluster analysis on the ECG signal in order to sort QRS complexes according to their morphology to normal and abnormal. It is used agglomerative hierarchical clustering and non-hierarchical method K – Means for which an application in Mathworks MATLAB programming equipment was developed. The first part deals with the theory of the ECG signal and cluster analysis, and then the second is the design, implementation and evaluation of the results of the usage of developed software on the ECG signal for the automatic division of QRS complexes into clusters.
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Text data clustering algorithms
Sedláček, Josef ; Burget, Radim (referee) ; Karásek, Jan (advisor)
The thesis deals with text mining. It describes the theory of text document clustering as well as algorithms used for clustering. This theory serves as a basis for developing an application for clustering text data. The application is developed in Java programming language and contains three methods used for clustering. The user can choose which method will be used for clustering the collection of documents. The implemented methods are K medoids, BiSec K medoids, and SOM (self-organization maps). The application also includes a validation set, which was specially created for the diploma thesis and it is used for testing the algorithms. Finally, the algorithms are compared according to obtained results.
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Acceleration of Algorithms for Clustering of Tunnels in Proteins
Jaroš, Marta ; Vašíček, Zdeněk (referee) ; Martínek, Tomáš (advisor)
This thesis deals with the clustering of tunnels in data obtained from the protein molecular dynamics simulation. This process is very computationaly intensive and it has been a challenge for scientific communities. The goal is to find such an algorithm with optimal time and space complexity ratio. The research of clustering algorithms, work with huge highdimensional datasets, visualisation and cluster-comparing methods are discussed. The thesis provides a proposal of the solution of this problem using the Twister Tries algorithm. The implementation details are analysed and the testing results of the solution quality and space complexity are provided. The goal of the thesis was to prove that we could achieve the same results with a stochastic algorithm - Twister Tries , as with an exact algorithm ( average-linkage ). This assumption was not confirmed confidently. Another finding of the hashing functions analysis shows that we could obtain the same results of hashing with a low dimensional hashing function but in much better computational time.
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Acceleration of Algorithms for Clustering of Tunnels in Proteins
Jaroš, Marta ; Vašíček, Zdeněk (referee) ; Martínek, Tomáš (advisor)
This thesis deals with the clustering of tunnels in data obtained from the protein molecular dynamics simulation. This process is very computationaly intensive and it has been a challenge for scientific communities. The goal is to find such an algorithm with optimal time and space complexity ratio. The research of clustering algorithms, work with huge highdimensional datasets, visualisation and cluster-comparing methods are discussed. The thesis provides a proposal of the solution of this problem using the Twister Tries algorithm. The implementation details are analysed and the testing results of the solution quality and space complexity are provided. The goal of the thesis was to prove that we could achieve the same results with a stochastic algorithm - Twister Tries , as with an exact algorithm ( average-linkage ). This assumption was not confirmed confidently. Another finding of the hashing functions analysis shows that we could obtain the same results of hashing with a low dimensional hashing function but in much better computational time.
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ECG Cluster Analysis
Pospíšil, David ; Kozumplík, Jiří (referee) ; Klimek, Martin (advisor)
This diploma thesis deals with the use of some methods of cluster analysis on the ECG signal in order to sort QRS complexes according to their morphology to normal and abnormal. It is used agglomerative hierarchical clustering and non-hierarchical method K – Means for which an application in Mathworks MATLAB programming equipment was developed. The first part deals with the theory of the ECG signal and cluster analysis, and then the second is the design, implementation and evaluation of the results of the usage of developed software on the ECG signal for the automatic division of QRS complexes into clusters.
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Text data clustering algorithms
Sedláček, Josef ; Burget, Radim (referee) ; Karásek, Jan (advisor)
The thesis deals with text mining. It describes the theory of text document clustering as well as algorithms used for clustering. This theory serves as a basis for developing an application for clustering text data. The application is developed in Java programming language and contains three methods used for clustering. The user can choose which method will be used for clustering the collection of documents. The implemented methods are K medoids, BiSec K medoids, and SOM (self-organization maps). The application also includes a validation set, which was specially created for the diploma thesis and it is used for testing the algorithms. Finally, the algorithms are compared according to obtained results.
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