One of the major issue for life insurance companies is proper and consistent valuation of liabilities. This thesis introduces the standard estimation methods used in practice and discussed the alternative methods, which might help to speed up these calculations. It studies two possible methods of acceleration of calcu- lations in life insurance: analytic function and cluster analysis. The outcome of these work is comparison of discussed methods applied on generated life insur- ance portfolio. All methods were applied on two possible insurance products. Comparison of the results is based on the calculation precision and time needed to process the liabilities of the insurance company's portfolio. 1

The master's thesis deals with answer of distribution of countries, which was determined in 1965 in the theoretical concept by John Hajnal, in contemporary Europe. The main aim was to reanalyze the original division of countries using cluster analysis on the basis demographic indicators: average age at first marriage men and women, the average age of a woman at first child birth, the number of divorces per 100 marriages, the proportion of live births in marriage and out of marriage. The data used came from the Eurostat database from 1990 to 2015. Cluster analyzes of European countries were also performed according to the value orientations of their inhabitants in the area of social relations and life expectations. Respondents' statements came from the European Social Survey from 2002 to 2018. Cluster analysis of selected demographic indicators did not confirm two models of Hajnal's concept of marital behavior. Cluster analyzes of respondents' value orientations confirmed the existence of two value approaches to life priorities - a preference for traditionally accepted values and a preference for a dynamic and efficient lifestyle. Keywords Hajnal line, family, marriage, divorce rate, ESS research project, K-means cluster analysis, values

The main aim of this diploma thesis was to explore the topic of malaria preventive measures. Concretely, to study which preventive measures are useful and to see how they are distributed around the world. For international organizations, this is very important as they need to know whether funds allocated for malaria aid are distributed effectively. This study is using manually compounded data from the World Health Organization for all countries threatened by malaria mostly from 2001 to 2018. For this purpose, panel data regression methods using robust standard errors, bootstrapping and cluster analysis were used. The results showed that generally, the most useful preventive measures are indoor-residual sprayings, a combination of sprayings and insecticide-treated nets and rapid diagnostic tests. Furthermore, the effect of the population living in rural areas is significant. Besides, gross domestic product is a very important factor for African countries. The stability analysis - bootstrapping - confirmed our results. However, we examined that insecticide-treated nets are still the most distributed measures. Doing the cluster analysis, we observed that countries on the same continent should not be treated similarly and we emphasized countries that should receive higher attention. Overall, the...

This work presents the topic of data mining on the web. It is focused on clustering. The aim of this project was to study the field of clustering and to implement clustering through the k-means algorithm. Then, the algorithm was tested on a dataset of text documents and on data extracted from web. This clustering method was implemented by means of Java technologies.

Reproductive health and induced abortion in Latin America and the Caribbean Abstract The objective of this study is to give a comprehensive overview of the reproductive health of the population living in Latin America and the Caribbean, and to evaluate the level of induced abortion in the region at the same time. In the theoretical part of the study the term reproductive health is defined and indicators evaluating its level are described. As well the difference between safe and unsafe abortion is explained and the state of abortion law is expounded in this part. Next part is devoted to the promotion of family planning programs. In the analytical part Latin American and Caribbean countries are divided into four groups by using cluster analysis. Within each group one selected country is characterized in more detail. Indicators entering into the analysis show the level of reproductive health, the level of fertility and the economic performance of countries in the early 21st century. In the part related to the induced abortion rate in the region is discovered that despite the fact that in Latin America and the Caribbean there is one of the highest levels of contraceptive prevalence, there is also the highest level of induced abortion in the world. In the last part of the study the relationship between level of...

Abstract Cluster analysis has become one of the main tools used in extracting knowledge from data, which is known as data mining. In this area of data analysis, data of large dimensions are often processed, both in the number of objects and in the number of variables, which characterize the objects. Many methods for data clustering have been developed. One of the most widely used is a k-means method, which is suitable for clustering data sets containing large number of objects. It is based on finding the best clustering in relation to the initial distribution of objects into clusters and subsequent step-by-step redistribution of objects belonging to the clusters by the optimization function. The aim of this Ph.D. thesis was a comparison of selected variants of existing k-means methods, detailed characterization of their positive and negative characte- ristics, new alternatives of this method and experimental comparisons with existing approaches. These objectives were met. I focused on modifications of the k-means method for clustering of large number of objects in my work, specifically on the algorithms BIRCH k-means, filtering, k-means++ and two-phases. I watched the time complexity of algorithms, the effect of initialization distribution and outliers, the validity of the resulting clusters. Two real data files and some generated data sets were used. The common and different features of method, which are under investigation, are summarized at the end of the work. The main aim and benefit of the work is to devise my modifications, solving the bottlenecks of the basic procedure and of the existing variants, their programming and verification. Some modifications brought accelerate the processing. The application of the main ideas of algorithm k-means++ brought to other variants of k-means method better results of clustering. The most significant of the proposed changes is a modification of the filtering algorithm, which brings an entirely new feature of the algorithm, which is the detection of outliers. The accompanying CD is enclosed. It includes the source code of programs written in MATLAB development environment. Programs were created specifically for the purpose of this work and are intended for experimental use. The CD also contains the data files used for various experiments.

Infant mortality in the Czech Republic and Europe: trends and patterns Abstract This Master's thesis addresses the development of infant mortality in the Czech Republic between the years 1950 and 2016 using more detailed indicators of infant mortality. To evaluate the impact of infant mortality rate, on the lengthening life expectancy at birth in the Czech Republic and selected European countries, a decomposition of life tables is included. Selected European countries are then compared with the use of cluster analysis, based on infant mortality indicators and life expectancy at birth by sex. Finally, an analysis is made on the basis of infant life tables which are calculated by sex, birth weight and legitimacy for the Czech Republic. Keywords: infant mortality, Czech Republic, European countries, tables of infant mortality, sex, child-legitimacy, birth weight, decomposition, cluster analysis

Association rules mining is one of several ways of knowledge discovery in databases. Paradoxically, data mining itself can produce such great amounts of association rules that there is a new knowledge management problem: there can easily be thousands or even more association rules holding in a data set. The goal of this work is to design a new method for association rules post-processing. The method should be software and domain independent. The output of the new method should be structured description of the whole set of discovered association rules. The output should help user to work with discovered rules. The path to reach the goal I used is: to split association rules into clusters. Each cluster should contain rules, which are more similar each other than to rules from another cluster. The output of the method is such cluster definition and description. The main contribution of this Ph.D. thesis is the described new Multicriterial clustering association rules method. Secondary contribution is the discussion of already published association rules post-processing methods. The output of the introduced new method are clusters of rules, which cannot be reached by any of former post-processing methods. According user expectations clusters are more relevant and more effective than any former association rules clustering results. The method is based on two orthogonal clustering of the same set of association rules. One clustering is based on interestingness measures (confidence, support, interest, etc.). Second clustering is inspired by document clustering in information retrieval. The representation of rules in vectors like documents is fontal in this thesis. The thesis is organized as follows. Chapter 2 identify the role of association rules in the KDD (knowledge discovery in databases) process, using KDD methodologies (CRISP-DM, SEMMA, GUHA, RAMSYS). Chapter 3 define association rule and introduce characteristics of association rules (including interestingness measuress). Chapter 4 introduce current association rules post-processing methods. Chapter 5 is the introduction to cluster analysis. Chapter 6 is the description of the new Multicriterial clustering association rules method. Chapter 7 consists of several experiments. Chapter 8 discuss possibilities of usage and development of the new method.

The study is concerned with corporate income taxes in the European Union and comparison these taxation systems. The theoretical part includes development of the harmonisation of the direct tax in the EU and a common consolidated corporate tax base. The study proceeds with the description of the corporate taxation system of the member states. The practical part is mainly dedicated to analysis of these taxation systems of the member countries. It describes elements of corporate income tax legislative and implicit tax rate and share corporate income tax on taxation mix. The analysis is processed in the computer program STATISTICA. The classify is made by cluster analysis that should find the similar taxation system. In conclusion, there are pointed out differences between member countries and an identification of the effects that cause differences.

Nowadays quality of life is often discussed topic. In defining this term, there is considerable ambiguity and disunity, since there is no universally accepted definition, nor theoretically sophisticated model. However, despite this fact, the level of quality of life is currently one of the most discussed topic. Monitoring the quality of life by using a variety of indicators are engaged in several international organizations, one of them is the Development Programme of the United Nations. This organization annually publishes the Human Development Index, which divides the world´s countries into four groups according to their level of development: low, medium, high and very high development. The aim of this thesis is to analyze the quality of life in 125 countries by using cluster analysis, accurately the Ward's method. Quality of life in this thesis is evaluated based on 19 demographic and economic indicators, which include life expectancy, literacy rate, access to drinking water and infant mortality rate. The cluster analysis divided the country into individual clusters by their similarities. Six clusters were created by this analysis, which had been compared with the results of Human Development Index. The clusters very well reflect the division, which is commonly used in the characterization of developing and developed countries. Each of the six clusters can be very well described and characterized in terms of quality of life. It is also possible qualify those clusters as poorest developing, low developed, moderately developed, medium development, high and very high development countries. Based on the results it can be stated that this analysis is consistent with other indicators of quality of life and the resulting clusters are identical with the division of countries which is commonly used.