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The Analysis of Security and Anonymity in Cryptocurrency Payments
Maňák, Michal ; Šebesta, Michal (advisor) ; Bruckner, Tomáš (referee)
Bitcoin and other cryptocurrencies are getting into common usage more and more frequently. Unfortunately this spread is connected with attempts to misuse their principles for personal benefit. The goal of this thesis is to explain the main principles of using bitcoin in relation to the internet identity. This connection is represented by two sources in the thesis: the first is a bitcoin blockchain, the second is the sum of various user profiles used in discussion forums and similar websites. The aim is to create and explain the process how to, by using the combination of this two sources, reveal the identity of a human using cryptocurrencies for payment (from a private person point of view). First, methods used for extraction of correct and useful data from the blockchain and internet profiles must be developed to be able to mine data from the joint database, merging into a full user profile containing personal data. Results of this investigation are extended by the rules of preserving anonymity on the internet, containing both technological and behavioural measures avoiding a share of personal data. The end of this thesis explains possible attack vectors that might be used for compromising cryptocurrencies. The methods of searching of the internet identity are a useful tool for facing these attacks.
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Discriminant and cluster analysis as a tool for classification of objects
Rynešová, Pavlína ; Löster, Tomáš (advisor) ; Řezanková, Hana (referee)
Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can be a disordered group of objects organized into several internally homogeneous classes or clusters. Discriminant analysis creates knowledge based on the jurisdiction of existing classes classification rule, which can be then used for classifying units with an unknown group membership. The aim of this thesis is a comparison of discriminant analysis and different methods of cluster analysis. To reflect the distances between objects within each cluster, squeared Euclidean and Mahalanobis distances are used. In total, there are 28 datasets analyzed in this thesis. In case of leaving correlated variables in the set and applying squared Euclidean distance, Ward´s method classified objects into clusters the most successfully (42,0 %). After changing metrics on the Mahalanobis distance, the most successful method has become the furthest neighbor method (37,5 %). After removing highly correlated variables and applying methods with Euclidean metric, Ward´s method was again the most successful in classification of objects (42,0%). From the result implies that cluster analysis is more precise when excluding correlated variables than when leaving them in a dataset. The average result of discriminant analysis for data with correlated variables and also without correlated variables is 88,7 %.
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Some Robust Estimation Tools for Multivariate Models
Kalina, Jan
Standard procedures of multivariate statistics and data mining for the analysis of multivariate data are known to be vulnerable to the presence of outlying and/or highly influential observations. This paper has the aim to propose and investigate specific approaches for two situations. First, we consider clustering of categorical data. While attention has been paid to sensitivity of standard statistical and data mining methods for categorical data only recently, we aim at modifying standard distance measures between clusters of such data. This allows us to propose a hierarchical agglomerative cluster analysis for two-way contingency tables with a large number of categories, based on a regularized measure of distance between two contingency tables. Such proposal improves the robustness to the presence of measurement errors for categorical data. As a second problem, we investigate the nonlinear version of the least weighted squares regression for data with a continuous response. Our aim is to propose an efficient algorithm for the least weighted squares estimator, which is formulated in a general way applicable to both linear and nonlinear regression. Our numerical study reveals the computational aspects of the algorithm and brings arguments in favor of its credibility.
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Segmentation of Infant Milks Market
Čevela, Josef ; Koudelka, Jan (advisor) ; Sobol, Robert (referee)
The principal goal of the master thesis is to explore the similarities and possible differences among customers of infant milks. Based on those findings, the customers are clustered into various segments, which are extremely homogenous within each one, but significantly heterogeneous outside each of them. The second goal of the thesis is to suggest the best marketing solutions for each revealed segment. The work is divided into two parts, theoretical and practical. The theoretical part describes the process of market segmentation. The characteristics of the infant milk market and the description of infant milk types are described in the practical part. The customers' segmentation is designed according to primary and secondary research. Secondary data is based on data from project MML-TGI and content analysis. The data from my own primary research are processed by IBM SPSS statistics version 20. Among the main finding of the thesis was defining 5 segments of the infant milk customers. The work characterizes all of them and special term is chosen for each of the segment. Finally, the relevant marketing solutions are suggested for each of the segment.
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Development of the European Union according to selected demographic indicators
Illeová, Iva ; Fiala, Tomáš (advisor) ; Miskolczi, Martina (referee)
This diploma aims to analyze the development of the European Union Member States for last thirty years in terms of demographic indicators. First chapter is focused on the historical development of the world's population, followed by the development of the European population and political history of the European Union. Next chapter is focused on the most commonly used demographic indicators for international comparisons divided into groups according to demographic processes, their interpretation and calculation. The main part of the work is analysis of selected indicators for the Member States of the EU with regard to the European average of 27 Member States in the period between 1981 and 2011. Another chapter is devoted to modify data for above mentioned simple analysis and cluster analysis, which is located in the last chapter. Cluster analysis was performed using Euclidean distance and using nearest and furthest neighbor and Ward's method. In 1981 the furthest neighbor method divided Member States into five clusters and in 2011 the Ward's method divided Member States again within five clusters, but with a different composition. Original data for Multidimensional description is added to annex with the results of calculations and graphs.
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Unemployment in Czech Republic and EU
Rytíř, Michal ; Arltová, Markéta (advisor) ; Helman, Karel (referee)
Unemployment is a common phenomenon in economy. The unemployment rate is an indicator reflecting the economic situation significantly. Unemployment is followed by the public intensively and that is why it is an important political topic. To fight unemployment it is necessary to analyze its current state, development and estimated future prospects. This thesis is focused on analysis of the state and development of unemployment in the Czech Republic and EU. Its future development is estimated using the Box-Jenkins method.
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Cluster analysis of European Union states using social and economical indicators
Černý, Andrej ; Löster, Tomáš (advisor) ; Bílková, Diana (referee)
Dissertation follows up cluster analysis of European Union states using social and economical indicators. In the first part all the social and economical indicators are defined. In the second part of the dissertation all these indicators in time of economical and financial and later debt crisis in years from 2007 to 2012 in European Union states are analysed. In the third part the cluster analysis is applicated for the social and economical indicators of the European Union states. Five clusters were identified using cluster analysis. Structure of these clusters was changing during years 2007 and 2012. First cluster contained developed Western European countries (Belgium, Denmark, Germany, Ireland, France, Netherlands, Austria, Finland, Sweden and United Kingdom). The second cluster was created from countries, that entered to European Union after year 2004 (Bulgaria, Estonia, Croatia, Latvia, Lithuania, Hungary, Poland, Romania and Slovakia). The third cluster contained Czech Republic, Estonia, Greece, Lithuania, Portugal, Slovakia and Slovenia. In the fourth cluster were Southern European countries Greece, Spain, Italy, Cyprus and Malta and the fifth cluster contained only Luxembourg.
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Cluster analysis of destricts of the Czech Republic ecording to demographic indicators
Saifrtová, Barbora ; Langhamrová, Jitka (advisor) ; Miskolczi, Martina (referee)
Master thesis deals with dividing destricts of the Czech Republic in to clusters acording to demographic indicators during the year 2011. After the theoretical introduction with exploratory data analyiss, factor analysis and cluster analysis is described practical implementation of agglomerative hierarchical clustering. Within the frame cluster analysis we compare results calculated by four methods of clustering, which are the single linkage method, the complete linkage method, the average linkage method and Ward's method. At the conclusion we select the method which divides destricts of the Czech Republic in to the clusters the best. Master thesis includes a prezentations of discovere results with the help of dendrograms and cartograms. The analysis were carried out with the help of the statistical program STATISTICA.
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Comparison of life quality in countries of European Union measured by various indicators
Knoll, Radim ; Malá, Ivana (advisor) ; Bílková, Diana (referee)
In this bachelor thesis I try to describe the quality of life using a wider range of components which affects the quality of life and use these components to compare the quality of life in 28 member countries of the European Union. This work is divided into four parts. The first part is a detailed description of individual components of life quality. Along with that a comparison of all indicators of life quality is shown. Compared indicators were used in multiple calculations and models of life quality. The second part contains an overall comparison of the European Union based on all indicators. The third part consists of a comparison of the values of selected indicators in years of 2007 and 2013. Furthermore cluster analysis which is based on similarity of states classified by chosen indicators is shown. The last part contains a list of individual states ranked by the quality of life in years of 2008 and 2012.
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Evaluation of the Success of Coefficients and Methods Used in Cluster Analysis
Hammerbauer, Jiří ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
The diploma thesis explores with the evaluation of the success of selected indices for determining the number of clusters used in cluster analysis. The aim of this thesis is on the basis of various combinations of clustering methods and distances verify whether, alternatively using which clustering methods and distances is it possible to rely on the results of indices for determining the number of clusters. The results of success rate presented in the third chapter suggest that not all of indices for determining the number of clusters can be used universally. The most successful index is Dunn index, which was able to determine the correct number of clusters in 37 % of cases, respectively Davies-Bouldin index with the share of 70 % when including deviation of one cluster. The success rate is affected by both used method and selected distance.
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