|
|
Optimization based on genetic algorithms for image registration
Horáková, Pavla ; Mézl, Martin (referee) ; Harabiš, Vratislav (advisor)
Diploma thesis is focused on global optimization methods and their utilization for medical image registration. The main aim is creation of the genetic algorithm and test its functionality on synthetic data. Besides test functions and test figures algorithm was subjected to real medical images. For this purpose was created graphical user interface with choise of parameters according to actual requirement. After adding an iterative gradient method it became of hybrid genetic algorithm.
|
|
|
Fuzzy Preference Structures in Multicriterial Decision Making
Majer, Tomáš ; Stryka, Lukáš (referee) ; Hliněná, Dana (advisor)
V mnohých rozhodovacích problémech evaluujeme akce z různých pohledů, které nazýváme kritéria. Například při ohodnocovaní auta uvažujeme kritéria jako maximální rychlost, cena, zrychlení a spotřeba. V obecnosti při rozhodování s ohledem na různá kritéria se setkáváme s problémem preferencí. Jedním z nejjednodušších řešení je vážený průměr uvažovaných kritérií. V této práci aplikujeme výsledky řešení jednoho multiriteriálního problému, přičemž porovnávání kritérií realizujeme použitím fuzzy preferenčních struktur. Naše řešení je ilustrováno na praktickém příkladě.
|
|
|
Clustering objects with the MCluster-Miner procedure of the LISp-Miner system
Pelc, Tomáš ; Šimůnek, Milan (advisor) ; Šulc, Zdeněk (referee)
This bachelor thesis deals with clustering objects with the MCluster-Miner procedure of the LISp-Miner system. The first aim of this bachelor thesis is clustering objects with the mentioned pro-cedure and analyzing its possible usage on different datasets. To achieve this goal, the procedure was applied on six different datasets. The secong aim of this thesis is to analyze and compare implemented algorithms, similarity measures and to propose recommendations for clustering parameters. To achieve this goal, the available algorithms and similarity measures are compared based on achieved results (the quality of distribution objects into clusters, the time of clustering task, the number of attributes used for clustering). Based on these comparisons, the recommen-dations for clustering parameters are proposed. The benefits of this thesis are these recommenda-tions, comparisons of available algorithms and similarity measures, summary of actual state (da-ted to May 2017) of the MCluster-Miner module and showing the possibility of displaying results of clustering task at the interactive analysis of geodata. The theoretical part comprises the description of the LISp-Miner system, basic clustering principles, clustering methods and similari-ty measures used by the GUHA-procedure MCluster-Miner, and the MCluster-Miner module. In the practical part the MCluster-Miner procedure is being applied on six different datasets and the achieved results are summarized there.
|
|
|
Míry podobnosti pro nominální data v hierarchickém shlukování
Šulc, Zdeněk ; Řezanková, Hana (advisor) ; Šimůnek, Milan (referee) ; Žambochová, Marta (referee)
This dissertation thesis deals with similarity measures for nominal data in hierarchical clustering, which can cope with variables with more than two categories, and which aspire to replace the simple matching approach standardly used in this area. These similarity measures take into account additional characteristics of a dataset, such as frequency distribution of categories or number of categories of a given variable. The thesis recognizes three main aims. The first one is an examination and clustering performance evaluation of selected similarity measures for nominal data in hierarchical clustering of objects and variables. To achieve this goal, four experiments dealing both with the object and variable clustering were performed. They examine the clustering quality of the examined similarity measures for nominal data in comparison with the commonly used similarity measures using a binary transformation, and moreover, with several alternative methods for nominal data clustering. The comparison and evaluation are performed on real and generated datasets. Outputs of these experiments lead to knowledge, which similarity measures can generally be used, which ones perform well in a particular situation, and which ones are not recommended to use for an object or variable clustering. The second aim is to propose a theory-based similarity measure, evaluate its properties, and compare it with the other examined similarity measures. Based on this aim, two novel similarity measures, Variable Entropy and Variable Mutability are proposed; especially, the former one performs very well in datasets with a lower number of variables. The third aim of this thesis is to provide a convenient software implementation based on the examined similarity measures for nominal data, which covers the whole clustering process from a computation of a proximity matrix to evaluation of resulting clusters. This goal was also achieved by creating the nomclust package for the software R, which covers this issue, and which is freely available.
|
|
|
Fuzzy Preference Structures in Multicriterial Decision Making
Majer, Tomáš ; Stryka, Lukáš (referee) ; Hliněná, Dana (advisor)
V mnohých rozhodovacích problémech evaluujeme akce z různých pohledů, které nazýváme kritéria. Například při ohodnocovaní auta uvažujeme kritéria jako maximální rychlost, cena, zrychlení a spotřeba. V obecnosti při rozhodování s ohledem na různá kritéria se setkáváme s problémem preferencí. Jedním z nejjednodušších řešení je vážený průměr uvažovaných kritérií. V této práci aplikujeme výsledky řešení jednoho multiriteriálního problému, přičemž porovnávání kritérií realizujeme použitím fuzzy preferenčních struktur. Naše řešení je ilustrováno na praktickém příkladě.
|
|
|
Optimization based on genetic algorithms for image registration
Horáková, Pavla ; Mézl, Martin (referee) ; Harabiš, Vratislav (advisor)
Diploma thesis is focused on global optimization methods and their utilization for medical image registration. The main aim is creation of the genetic algorithm and test its functionality on synthetic data. Besides test functions and test figures algorithm was subjected to real medical images. For this purpose was created graphical user interface with choise of parameters according to actual requirement. After adding an iterative gradient method it became of hybrid genetic algorithm.
|
|
| |
|
| |
guest ::
