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Hierarchical Image Segmentation
Staněk, Stanislav ; Švub, Miroslav (referee) ; Španěl, Michal (advisor)
In many vision applications image segmentation is one of the most critical steps of analysis,which has the objective of extracting information from an image. In this work a segmentation method based upon fuzzy c-means and k-means clustering is presented. A hierarchical data structure together with clustering algorithms for the segmentation in each level of the pyramid is used.The results show that the computation time is much less then that of a classical clustering.
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Text mining focused on clustering and fuzzy clustering methods
Zubková, Kateřina ; Karpíšek, Zdeněk (referee) ; Žák, Libor (advisor)
This thesis is focused on cluster analysis in the field of text mining and its application to real data. The aim of the thesis is to find suitable categories (clusters) in the transcribed calls recorded in the contact center of Česká pojišťovna a.s. by transferring these textual documents into the vector space using basic text mining methods and the implemented clustering algorithms. From the formal point of view, the thesis contains a description of preprocessing and representation of textual data, a description of several common clustering methods, cluster validation, and the application itself.
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Text mining focused on clustering and fuzzy clustering methods
Zubková, Kateřina ; Karpíšek, Zdeněk (referee) ; Žák, Libor (advisor)
This thesis is focused on cluster analysis in the field of text mining and its application to real data. The aim of the thesis is to find suitable categories (clusters) in the transcribed calls recorded in the contact center of Česká pojišťovna a.s. by transferring these textual documents into the vector space using basic text mining methods and the implemented clustering algorithms. From the formal point of view, the thesis contains a description of preprocessing and representation of textual data, a description of several common clustering methods, cluster validation, and the application itself.
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Hierarchical Image Segmentation
Staněk, Stanislav ; Švub, Miroslav (referee) ; Španěl, Michal (advisor)
In many vision applications image segmentation is one of the most critical steps of analysis,which has the objective of extracting information from an image. In this work a segmentation method based upon fuzzy c-means and k-means clustering is presented. A hierarchical data structure together with clustering algorithms for the segmentation in each level of the pyramid is used.The results show that the computation time is much less then that of a classical clustering.
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Optimalizace rozvržení provozu ve firmě Vodárenská akciová společnost a.s.
Urbanová, Zuzana
Dimploma thesis deals with the optimization of operation distribution in company Vodárenská akciová společnost, a.s. Aim of the thesis is to determine achievement standards of companies branches and to state the capacity reserves for every one of them. Next, using methods of cluster analysis and graph theory, proposing recommendation leading to effective utilization of operation capacity. This is done by optimizing total number of business branches and subsequent creation of new regions. Thesis consists of theoretical and practical part. In this papers theoretical part, hierarchical and nonhierarchical clustering algorithms, minimal spanning tree and water management sector are described. Practical part addresses optimization of layout of company operation distribution. Based on comparison of outputs of chosen methods, recommendations for company, will be proposed.
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