The main focus of this thesis is to find out what factors have the biggest influence on the price of electricity for household consumers in the European Union in the context of creating the internal electricity market in the EU. By means of the cluster analysis six EU Member States have been selected according to the following criteria: electricity consumption, electricity production, and the price of electricity. As a result of that Belgium, the Czech Republic, Estonia, Hungary, Malta and Sweden have been selected. Consequently, the regression analysis has been carried out to find out what factors influence the electricity prices in every country individually. The independent variables are mainly various sources of electricity production. It was found out those renewable resources most influent the electricity price for households in Belgium, Estonia, Hungary, and Sweden. Nevertheless, in the context of Europe 2020 strategy and the formation of the European energy market, one of the European Commission's objectives is to increase the share of renewable sources in the production of electricity in the EU Member States. Therefore, it will bring about higher prices of electricity, which goes against the Commission's effort to decrease the price of electricity for households through the liberalisation of electricity and the creation of the internal electricity market.

The main goal of this Master's Thesis is to form marketing recommendations for maternity hospitals. These marketing recommendations are in the form of marketing mixes, that are designed for individual revealed segments. The theoretical part of my Master's Thesis contains the reason why the maternity hospitals should implement marketing in the present day. Further goal of the theoretical part is to explain why I apply to the area of obstetrics the marketing of services. Further the theoretical part describes specifics of marketing of services, segmentation process, targeting and positioning. There is stated characteristics of the marketing mix in the area of services at the end of the theoretical part. The analytical part is based on written questionnaires inquiry. On the basis of results of questionnaires inquiry is carried out the process of segmentation using statistical programme IBM SPSS Statistics version 21.0. The result of segmentation process is discovering three market segments. These segments show similar characteristics. There is determined attractiveness of revealed segments in the chapter dealing with targeting. Marketing recommendations describe the value of providing services. Marketing recommendations are supplemented with social status of respondents.

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The presented thesis discusses the basic analysis methods of arteriovelocitograms. The core of this work rests in classification of signals and contribution to possibilities of noninvasive diagnostic methods for evaluation patients with peripheral ischemic occlusive arterial disease. The classification employs multivariate statistical methods and principles of neural networks. The data processing works with an angiographic verified set of arteriovelocitogram dates. The digital subtraction angiography classified them into 3 separable classes in dependence on degree of vascular stenosis. Classification AVG signals are represented in the program by the 6 parameters that are measured on 3 different places on each patient’s leg. Evaluation of disease appeared to be a comprehensive approach at signals acquired from whole patient’s leg. The sensitivity of clustering method compared with angiography is between 82.75 % and 90.90 %, specificity between 80.66 % and 88.88 %. Using neural networks sensitivity is in range of 79.06 % and 96.87 %, specificity is in range of 73.07 % and 91.30 %.

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.

This work is dedicated to dynamic time warping in biosignal processing, especially it´s application for ECG signals. On the beginning the theoretical notes about cardiography are summarized. Then, the DTW analysis follows along with conditions and demands assessments for it’s successful application. Next, several variants and application possibilities are described. The practical part covers the design of this method, the outputs comprehension, settings optimization and realization of methods related with DTW

The Master’s thesis deals with the issue of collecting mixed municipal waste from producers to a~waste-to-energy or landfills. The initial chapters are aimed to waste legislation and transportation of the waste by road freight transport across Europe. The objective is to collect the data, that are required for calculation in tool NERUDA. The next part describes the cluster analysis and different approaches in it. The selected methods of cluster analysis are apllied to the logistic task in the final chapters. The cluster analysis is considered from different aspects. The results are visualized using the software ArcGIS.

This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.

This master's thesis deals with with FIT-Miner, the system for knowledge discovery in databases. The first part of this paper describes the data-mining process, mixture model's issues and FIT-Miner system. Second part deals with design, implementation and testing of created module, which is used for cluster analysis with Expectation-Maximalization algorithm. The end of the paper is focused to design of modules using Java Store Procedures Technology.

This master's thesis deals with development of a module for a data mining system, which is being developed on FIT. The first part describes the general knowledge discovery process and cluster analysis including cluster validation; it also describes Oracle Data Mining including algorithms, which it uses for clustering. At the end it deals with the system and the technologies it uses, such as NetBeans Platform and DMSL. The second part describes design of a clustering module and a module used to compare its results. It also deals with visualization of cluster analysis results and shows the achievements.