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Segmentation of the Single-Family Housing Market
Bondareva, Anna ; Koudelka, Jan (advisor) ; Stříteský, Václav (referee)
The main goal of this thesis is to test the possibility of forward and backward segmentation on the single-family housing market, to reveal, describe and develop a profile of segments and to suggest marketing recommendations. Data from primary research is encoded in MS Excel and processed in the statistical analysis program SPSS Statistics 19 for Windows. The output of the thesis reveals three forward segments and three backward segments of the market. Based on certain specifics shown by each of the monitored segments, I suggested numerous marketing recommendations.
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Post-processing of association rules by multicriterial clustering method
Kejkula, Martin ; Rauch, Jan (advisor) ; Berka, Petr (referee) ; Máša, Petr (referee)
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.
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Evaluation of Cluster Analysis Methods
Löster, Tomáš ; Řezanková, Hana (advisor) ; Berka, Petr (referee) ; Dohnal, Gejza (referee)
Cluster analysis includes a range of methods and practices that are used primarily for classification of objects. It takes an important role in many areas. Since the resulting distribution of objects into clusters may vary depending on the selected methods and specifications, it is appropriate to assess the results obtained. This paper proposes new ways of evaluating these results in a situation where objects are characterized by qualitative variables or by variables of different types. These coefficients can be used either to compare different methods (in terms of better outcomes) or for finding of the optimal number of clusters. All of them are based on the detection of variability which is also used for measuring of dissimilarity of objects and clusters. The newly proposed evaluation methods are applied to real data sets (of different sizes, with different number of variables, including variables of different types) and the behavior of these coefficients in different conditions is being examined. These data sets have known as well as unknown classification of objects into clusters. The best coefficient for evaluating clustering results with different types of variables can be considered, based on the analysis carried out, the modified coefficient of CHF. Local maximum value according to which the results of the clustering are evaluated, almost always exists. The analysis has proven that in most cases this value meets the expected results of the well-known classification of objects into clusters. The existence of local extremes of the other coefficients depends on specific data sets and is not always feasible.
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Cluster analysis of large data sets: new procedures based on the method k-means
Žambochová, Marta ; Řezanková, Hana (advisor) ; Húsek, Dušan (referee) ; Antoch, Jaromír (referee)
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.
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Investing posibilities of citizen in the Czech Republic
Nocar, Jan ; Bartošová, Jitka (advisor) ; Jelínek, Jiří (referee)
This thesis discusses the options households have when it comes to investing in capital markets in the Czech Republic. The issue of investing and capital market options is analyzed. Following this analysis comes the description of financial instruments, their characteristics, and the usability of these instruments by small investors. On the basis of the theory presented, a study was conducted to examine the usage of individual financial products. The collected data was processed using modern software tools, which helped in drawing several conclusions, results, and recommendations for investors and financial instrument providers alike.
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Determinants of consumer behavior in relation to sports activities
Kafková, Petra ; Koudelka, Jan (advisor) ; Susnyak, Viktor (referee)
The aim of this Master's Thesis is to reveal and describe segments within a group of university students. The first part includes the theory of market segmentation, including the market research theory and description of the technique of cluster analysis. The second part contains the exploration of secondary data - identifying characteristics of the target group and searching for ideas for own research. The third part describes preparation for primary research, collecting of the data and their analysis. At the end I reveal segments with the help of cluster analysis and describe all uncovered segments.
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Segmentation of the facial care market
Fialová, Zdeňka ; Koudelka, Jan (advisor) ; Zamazalová, Marcela (referee)
The main goal of the Master's Thesis is to discover significant differences in consumers' behaviour. Based on these differences it determinates and describes the segmenents of consumers. An important goal is to design marketing strategies for these segments as well. The theoretical part of the thesis includes the explanation of the segmentation process. The analytical part covers the characteristics of the Czech facial care market and the analysis of the Market&Media&Lifestyle data. The practical part of the thesis focuses on the process of segmentation using questionares and the IBM SPSS Statistics programme. The output of the thesis reveals three segments of the market and suggests relevant marketing strategies for them.
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The European Market Segmentation with Respect to the Post-Productive Population
Rudá, Eliška ; Vošta, Milan (advisor) ; Kašpar, Václav (referee)
The first step of the hierarchical approach to the international market segmentation is described and demonstrated in this master's thesis. The theoretical part that covers the methodology, statistical background and secondary data sources is followed by the illustration of cluster analysis. The outcome is the division of thirty European countries into five segments with respect to the post-productive population. The formed segments are then characterised and interpreted. Their usability is shown in three challenges in the European Year of 2012, which is dedicated to Active Ageing.
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Beer Market Segmentation
Koulová, Tereza ; Filipová, Alena (advisor) ; Zeman, Jiří (referee)
This master thesis deals with beer market segmentation in the Czech Republic. The main aim of this thesis is to find out whether there is only one big segment or more different segments can be found there. The thesis is divided into two parts, the methodological --theoretical and the practical one. Segmentation, targeting and positioning are described in the first part. The second part is devoted to secondary resources analysis and content analysis focused on beer advertisements. The main part of practical section is to depicts primary quantitative research which is the basis for market segmentation done via SPSS statistic programme. At the end of this master thesis all segments are described in detail and marketing recommendation for each of them are also added there.
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EVALUATION AND CLASSIFICATION OF THE EUROPEAN UNION COUNTRIES
Brabcová, Petra ; Löster, Tomáš (advisor) ; Vltavská, Kristýna (referee)
This diploma work describes the classification of the member states of the European Union according to the demographic indicators. It evaluates development in the individual states by absolute demographic indicators too. In the year 2008 less children were born and less people were died in the most of the member states than the year 1993. The hope of the end of life grows up in all contries. Relative demographic indicators are used in the cluster analysis for diversification of rhe states into certain groups in accordance with their similarity. Two methods are used in this work --of the farthest neighbour and the Ward method (the hierarchical clustering method), the both with the Euclid distance. The hierarchical method of the farthest neighbour divided fifteen states into the four clusters in 1995, twenty-five states into the six clusters in 2004 and twenty-seven states into the six clusters in 2007. The Ward method divided these states into the three clusters in 1995, into the six clusters in 2004 and into the three clusters in 2007.
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