The main goal of my master thesis is to identify differences, or similarities among consumers of infant food. Based on these characteristics were revealed groups, also called segments. Consumers in these segments are as homogeneous as possible, and vice versa the segments are mutually the most heterogeneous. After uncovering these segments is another goal of proposing marketing recommendations to individual segments. The work consists of theoretical and practical part. The theoretical part describes the process of segmentation. Practical part deals with the uncovering of segments. The research used secondary and primary data. Were used a secondary data from the database MML-TGI, which was analyzed by the Data Analyzer. Primary data was collected by questionnaire survey and subsequently processed in the IBM SPSS Statistics version 22. There has been uncovering the four segments, which have been named, described and suggested marketing recommendation.

This thesis is focus on the book market in Czech Republic and its current situation. The main objective of the thesis is, through primary research and cluster analysis, to uncover market segments, which shows significant differences in costumer behavior and through crosstabs develop profile of these groups. Base on this information focus on the segments attractive for bookshops to address them and prepare recommendation to focus on them. The market research has a qualitative character and is realized inform of personal and internet questionnaire. Selection of respondent is in form of Quota sampling, using quotas tender, age and education.

Ph.D. students are an integral part of the tertiary education system. Encouragement for doctoral programs and their students is very important because they are the ones who will participate in research projects in the future and they will contribute to society as a whole. The majority of scholarships for Ph.D. students comes from public sources. An important question to be asked is whether the scholarships are sufficient to finance Ph.D. studies and whether there are differences in the amount depending on gender, field of study or region. This thesis aims to answer these questions by applying statistical methods to the results of the survey DOKTORANDI 2014.

Data mining is a new discipline that occurs with increasing amount of stored data and the increasing need to obtain the information hidden in them. It is focused on the mining of potentially useful information from large data sets and it lies at the intersection of statistics, machine learning, artificial intelligence, databases and other areas. The aim of this thesis is to present the process of data mining with an emphasis on its connection with statistics and to describe a selection of statistical methods widely used in this field and which were also used in the applied data mining problem in this thesis. Real data from purchases in the online store show that using different methods gives different results and interesting information about purchasing behavior, and also proves that not all methods are always applicable to all types of tasks.

The goal of this work is to analyze the evolution of household consumption of the states in the EU. The consumption will be researched in the view of classification COICOP, which is the classification of individual consumption by purpose. After mapping of this evolution the estimation of future values will be done from known time series. This estimation will be performed by two different ways. First one will respect the composition of household consumption in sections of classification COICOP. The second one will only work with time series of average consumption for all sections together. To compare the states cluster analysis will be done. This analysis will be done by two ways again. First one will be aimed to analyze the current situation and the second one will be aimed to analyze the evolution of household consumption. Instead of Microsoft Excel STATGRAPHICS X64 CENTURION and SPSS will be used in this thesis. Household consumption prognosis is the main benefit of this thesis. This prognosis is made for all sections of COICOP. Analysis has shown, that the consumption should rise in future. There are few exceptions, mainly countries with not good economic situation as Greece.

This diploma thesis aims to identify collaboration factors within a joint NGO project called Reconstruction of the State. The goal is to find out the factors that led to the establishment of this joint project as perceived by representatives of the cooperating organizations. Another objective is to discover a possible pattern of collaboration factors within this project. The theoretical part introduces the main theories of interorganizational cooperation and the conceptualization of collaboration factors that have been described in these theories. The conceptualization of the collaboration factors forms the basis of a questionnaire issued to the NGOs' representatives. The results of the survey are used as input in a hierarchical cluster analysis that represents the key part of the empirical part of this thesis. The results of the cluster analysis are thus derived from the responses given by representatives of the 13 NGOs involved in the Reconstruction of the State project and they identify the key collaboration factors as well as their configuration. The final section presents and further interprets the findings resulting from the cluster analysis within the chosen theoretical framework. Key words: collaboration factors, organizational resources, NGOs, interorganizational collaboration, cluster analysis

The aim of the first part of this thesis is to explain the theoretical basis of transmission electron microscopy and to mention fundamental parts of transmission electron microscopes. The next part of this work is focused on possible methods of image segmentation, the use of neural networks in the detection of objects in an image and the subsequent clustering of results. The theoretical part of the thesis is concluded with an explanation of some already published methods of automatic detection of biological structures in microscopic images and theoretical design of the algorithm, which will be subsequently developed. The process of training neural networks in order to automatically detect biological structures in an image is described at the beginning of the practical part. This is followed by an evaluation of the results achieved by these networks. Subsequently, cluster analysis methods are applied to these results, the products of which are compared with each other and also with the results obtained by already published methods.

Hadoop is a popular software framework written in Java that performs data-intensive distributed computations on a cluster. It includes Hadoop MapReduce and the Hadoop Distributed File System (HDFS). HDFS has known scalability limitations due to its single NameNode which holds the entire file system namespace in RAM on one computer. Therefore, the NameNode can only store limited amounts of file names depending on the RAM capacity. The solution to furthering scalability is distributing the namespace similar to how file is data divided into chunks and stored across cluster nodes. Hadoop has an abstract file system API which is extended to integrate HDFS, but has also been extended for integrating file systems S3, CloudStore, Ceph and PVFS. File systems Ceph and PVFS already distribute the namespace, while others such as Lustre are making the conversion. Google previously announced in 2009 they have been implementing a Google File System distributed namespace to achieve greater scalability. The Generic Hadoop API is created from Hadoop's abstract file system API. It speaks a simple communication protocol that can integrate any file system which supports TCP sockets. By providing a file system agnostic API, future work with other file systems might provide ways for surpassing Hadoop 's current scalability limitations. Furthermore, the new API eliminates the need for customizing Hadoop's Java implementation, and instead moves the implementation to the file system itself. Thus, developers wishing to integrate their new file system with Hadoop are not responsible for understanding details ofHadoop's internal operation. The API is tested on a homogeneous, four-node cluster with OrangeFS. Initial OrangeFS I/0 throughputs compared to HDFS are 67% ofHDFS' write throughput and 74% percent of HDFS' read throughput. But, compared with an alternate method of integrating with OrangeFS (a POSIX kernel interface), write and read throughput is increased by 23% and 7%, respectively

This work deals with delineation of ECG signals. First we will become familiar with ECG and commonly used processing methods. Various transformation methods of ECG leads. Further we will describe methods of delineation ECG signals. Second part is dedicated to metod used in this work for delineation of ECG signals from CSE database. Particulary method using Dyadic Wavelet Transform. Work include, description of used program and results. Third part is dedicated to methods of ECG leads transformations and cluster analysis. In conclusion we will evaluate the results.

This term project deals with the classification of 1D gel electrophoresis samples. It describes the theoretical information about gel electrophoresis, various types of errors, processing of the image and its classification using the cluster analysis. One of the main goals is creation of images with the highest quality as possible. A realization of pre-processing and detection of the sample borders is made in the MATLAB environment. And finally, classification of samples is done with subsequent statistical analysis.