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RBF-networks with a dynamic architecture
Jakubík, Miroslav ; Mrázová, Iveta (advisor) ; Kukačka, Marek (referee)
In this master thesis I recapitulated several methods for data clustering. Two well known clustering algorithms, concretely K-means algorithm and Fuzzy C-means (FCM) algorithm, were described in the submitted work. I presented several methods, which could help estimate the optimal number of clusters. Further, I described Kohonen maps and two models of Kohonen's maps with dynamically changing structure, namely Kohonen map with growing grid and the model of growing neural gas. At last I described quite new model of radial basis function neural networks. I presented several learning algorithms for this model of neural networks, RAN, RANKEF, MRAN, EMRAN and GAP. In the end of this work I made some clustering experiments with real data. This data describes the international trade among states of the whole world.
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Clustering of dynamic data
Marko, Michal ; Mráz, František (advisor) ; Skopal, Tomáš (referee)
Title: Cluster analysis of dynamic data Author: Bc. Michal Marko Department: Department of Software and Computer Science Education Supervisor: RNDr. František Mráz, CSc. Supervisor's e-mail address: Frantisek.Mraz@mff.cuni.cz Abstract: The mail goal of this thesis is to choose or eventually to propose own modifications to some of the cluster analysis methods in order to observe the progress of dynamic data and its clusters. The chosen ones are applied to the real data. The dynamic data denotes series of information that is created periodically over the time describing the same characteristics of the given set of data objects. When applied to such data, the problem of classic clustering algorithm is the lack of coherence between the results of particular data set from the series which can be illustrated via application to our artificial data. We discuss the idea of proposed modifications and compare the progress of the methods based on them. In order to be able to use our modified methods on the real data, we examine their applicability to the multidimensional artificial data. Due to the complications caused by multidimensional space we develop our own validation criterion. Once the methods are approved for use in such space, we apply our modified methods on the real data, followed by the visualization and...
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Text mining in social network analysis
Hušek, Michal ; Mrázová, Iveta (advisor) ; Pešková, Klára (referee)
Title: Text mining in social network analysis Author: Bc. Michal Hušek Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: doc. RNDr. Iveta Mrázová, CSc., Department of Theoretical Computer Science and Mathematical Logic Abstract: Nowadays, social networks represent one of the most important sources of valuable information. This work focuses on mining the data provided by social networks. Multiple data mining techniques are discussed and analysed in this work, namely, clustering, neural networks, ranking algorithms and histogram statistics. Most of the mentioned algorithms have been implemented and tested on real-world social network data and the obtained results have been mutually compared against each other whenever it made sense. For computationally demanding tasks, graphic processing units have been used in order to speed up calculations for vast amounts of data, e.g., during clustering. The performed tests have confirmed lower time requirements. All the performed analyses are, however, independent of the actually involved type of social network. Keywords: data mining, social networks, clustering, neural networks, ranking algorithms, CUDA
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RBF-networks with a dynamic architecture
Jakubík, Miroslav ; Mrázová, Iveta (advisor) ; Kukačka, Marek (referee)
In this master thesis I recapitulated several methods for data clustering. Two well known clustering algorithms, concretely K-means algorithm and Fuzzy C-means (FCM) algorithm, were described in the submitted work. I presented several methods, which could help estimate the optimal number of clusters. Further, I described Kohonen maps and two models of Kohonen's maps with dynamically changing structure, namely Kohonen map with growing grid and the model of growing neural gas. At last I described quite new model of radial basis function neural networks. I presented several learning algorithms for this model of neural networks, RAN, RANKEF, MRAN, EMRAN and GAP. In the end of this work I made some clustering experiments with real data. This data describes the international trade among states of the whole world.
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RBF-networks with a dynamic architecture
Jakubík, Miroslav ; Mrázová, Iveta (advisor) ; Kukačka, Marek (referee)
In this master thesis I recapitulated several methods for clustering input data. Two well known clustering algorithms, concretely K-means algorithm and Fuzzy C-means (FCM) algorithm, were described in the submitted work. I presented several methods, which could help estimate the optimal number of clusters. Further, I described Kohonen maps and two models of Kohonen's maps with dynamically changing structure, namely Kohonen map with growing grid and the model of growing neural gas. At last I described quite new model of radial basis function neural networks. I presented several learning algorithms for this model of neural networks. In the end of this work I made some clustering experiments with real data. This data describes the international trade among states of the whole world.
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|
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Clustering of dynamic data
Marko, Michal ; Mráz, František (advisor) ; Skopal, Tomáš (referee)
Title: Cluster analysis of dynamic data Author: Bc. Michal Marko Department: Department of Software and Computer Science Education Supervisor: RNDr. František Mráz, CSc. Supervisor's e-mail address: Frantisek.Mraz@mff.cuni.cz Abstract: The mail goal of this thesis is to choose or eventually to propose own modifications to some of the cluster analysis methods in order to observe the progress of dynamic data and its clusters. The chosen ones are applied to the real data. The dynamic data denotes series of information that is created periodically over the time describing the same characteristics of the given set of data objects. When applied to such data, the problem of classic clustering algorithm is the lack of coherence between the results of particular data set from the series which can be illustrated via application to our artificial data. We discuss the idea of proposed modifications and compare the progress of the methods based on them. In order to be able to use our modified methods on the real data, we examine their applicability to the multidimensional artificial data. Due to the complications caused by multidimensional space we develop our own validation criterion. Once the methods are approved for use in such space, we apply our modified methods on the real data, followed by the visualization and...
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