|
|
Czech consumer leisure analysis
Jansa, Hynek ; Chytková, Zuzana (advisor) ; Černá, Jitka (referee)
The aim of the thesis is to make segmentation of leisure activities market, to uncover important customer segments and describe the differences in their behavior and feature. Is used the knowledge of social sciences, particularly economics and sociology, with the help of which is described relationship of lifestyle and consumer behavior. The data come from research focused on consumer and media behavior and its relation to population lifestyle. Data were subjected to factor and cluster analysis.
|
|
|
Cluster analysis as a tool for object classification
Vanišová, Adéla ; Löster, Tomáš (advisor) ; Bílková, Diana (referee)
The aim of this thesis is to examine the cluster analysis ability segment the data set by selected methods. The data sets are consisting of quantitative variables. The basic criterion for the data sets is that the number of classes has to be known and the next criterion is that the membership of all object to each class has to be known too. Execution of the cluster analysis was based on knowledge about the number of classes. Classified objects to individual clusters were compared with its original classes. The output was the relative success of classification by selected methods. Cluster analysis methods are not able to determine an optimal number of clusters. Estimates of the optimal number of clusters were the second step in analysis for each data set. The ability of selected criteria identify the original number of classes was analyzed by comparing numbers of original classes and numbers of optimal clusters. The main contribution of this thesis is the validation of the ability of selected cluster analysis methods to identify similar objects and verify the ability of selected criteria to estimate the number of clusters corresponding to the real file distribution. Moreover, this work provides a structured overview of the basic cluster analysis methods and indicators for estimating the optimal number of clusters.
|
|
|
Market segmentation of plastic surgery in the Czech Republic
Maříková, Pavla ; Koudelka, Jan (advisor) ; Vávra, Oldřich (referee)
Main objective of my thesis is to recommend a marketing orientation for clinics through an understanding of why men and women undergo plastic surgery and what is their reason to start considering it. Because women use plastic surgery more than men, the study is mainly focused on them. Another target is to compare competing invasive and non-invasive cosmetic treatments. The research part is consisting of a quantitative research. Quantitative research is based on online questionnaire and distributes plastic surgery market, with potential candidates and clients who have already undergone some treatment to the individual segments. Individual interesting reliances between them are pointed out with cross-analyzing by Excel program. For segmentation process was used IBM SPSS Statistics program. Outcome of this thesis discovers five segments and subsequent recommendations for marketing orientation for selected segments.
|
|
|
Segmentation of the tablet chocolate market
Špidlová, Veronika ; Koudelka, Jan (advisor) ; Pešek, Ondřej (referee)
The main goal of this Master's Thesis is to discover and describe significant differences in consumer's behavior on the Czech tablet chocolate market. Based on these found differences reveal, characterize and develop segment's profiles. Further goal is to design relevant marketing recommendations for these segments. The theoretical part contains the explanation of the segmentation process. The analytical part includes main characteristics of the Czech tablet chocolate market and the analysis of the secondary and primary data. The market segmentation was performed based on the results of these analyses. For the segmentation process was used the IBM SPSS Statistics program. The outcome of the thesis discovers four market segments and proposes corresponding marketing strategies for them.
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
guest ::
