The thesis "Actual role of knowledge discovery in databases˝ is concerned with churn prediction in mobile telecommunications. The issue is based on real data of a telecommunication company and it covers all steps of data mining process. In accord with the methodology CRISP-DM, the work looks thouroughly at the following stages: business understanding, data understanding, data preparation, modeling, evaluation and deployment. As far as a system for knowledge discovery in databases is concerned, the tool IBM SPSS Modeler was selected. The introductory chapter of the theoretical part familiarises the reader with the issue of so called churn management, which comprises the given assignment; the basic concepts related to data mining are defined in the chapter as well. The attention is also given to the basic types of tasks of knowledge discovery of databasis and algorithms that are pertinent to the selected assignment (decision trees, regression, neural network, bayesian network and SVM). The methodology describing phases of knowledge discovery in databases is included in a separate chapter, wherein the methodology of CRIPS-DM is examined in greater detail, since it represents the foundation for the solution of our practical assignment. The conclusion of the theoretical part also observes comercial or freely available systems for knowledge discovery in databases.

This thesis aims to describe the theoretical principles of customer segmentation using modern methods of cluster analysis and their practical verification in a commercial environment of utility company. The theoretical part of the thesis describes the principles of clustering analysis using K-means algorithm and hierarchical clustering algorithms. The SW support chapter deals with the possibilities of R, Rapid Miner and KXEN software tools. For the purpose of data processing the theoretical part describes SEMMA, 5A and CRISP methodologies. The practical part is focused on the analysis of homogeneity/heterogeneity of the energy market in the household segment in the utility company EON. Using data from company´s customer system, available external data and programming language R a hierarchical cluster analysis is processed. Through the analysis of gained clusters there are identified the main characteristics of particular clusters which are consequently examined from marketing usage perspectives.

Most of the modern cities are today facing tremendous traffic congestions, which is a consequence of an increasing usage of private motor vehicles in the cities. Public transport plays a crucial role to reduce this traffic, but to be an attractive alternative to the use of private motor vehicles the public transport needs to provide services that suit the citizens requirements for travelling. A system that can provide transit agencies with rapid feedback about the usage of their transport network is the Automatic Passenger Counting (APC) system, a system that registers the number of passengers boarding and alighting a vehicle. Knowledge about the passengers travel behaviour can be used by transit agencies to adapt and improve their services to satisfy the requirements, but to achieve this knowledge transit agencies needs to know how to use an APC system. This thesis investigates how a transit agency can make use of an APC system. The research has taken place in Melbourne where Yarra Trams, operator of the tram network, now are putting effort in how to utilise the APC system. A theoretical framework based on theories about Knowledge Discovery from Data, System Development, and Human Computer Interaction, is built, tested, and evaluated in a case study at Yarra Trams. The case study resulted in a software system that can process and model Yarra Tram's APC data. The result of the research is a proposal of a framework consistingof different steps and events that can be used as a guide for a transit agency that wants to make use of an APC system.

This master thesis deals with automation process of a data mining in the LISp-Miner program. The aim of this thesis is to create an automated process that analyzes analytical questions in the data about traffic accidents in the Czech Republic using a LMCL scripting language and LM Exec module. Theoretical part of thesis describes the process of knowledge discovery in databases and most widely used methodology. It also describes the relevant topics for the work with LISp-Miner. The practical part is focused on description of traffic accidents in the Czech Republic, a description of the used data, creation and evaluation of analytical questions and especially a description of created scripts. The output of the thesis is a group of scripts and manual how to use them again, so they can be reused for analysis of actual data on traffic accidents not only in the Czech Republic, if they have the same data structure.

The thesis deals with the valuation of real estates in the Czech Republic using statistical methods. The work focuses on a complex task based on data from an advertising web portal. The aim of the thesis is to create a prototype of the statistical predication model of the residential properties valuation in Prague and to further evaluate the dissemination of its possibilities. The structure of the work is conceived according to the CRISP-DM methodology. On the pre-processed data are tested the methods regression trees and random forests, which are used to predict the price of real estate.

Diploma thesis " The real role of knowledge mining " is divided into two major parts, the theoretical and the practical. The practical part describes the basic concepts of data mining, various methods and types of tasks used for knowledge discovery in databases and algorithms used in this area . Main focus is devoted to the CRISP -DM methodology and to various stages of knowledge discovery from databases. This methodology will be later used as the basis for practical part of the thesis while other less known methods used for data mining won`t be neglected. List of paid and free software which can be used for knowledge mining in databases is presented at the end of theoretical part. The second part of the thesis is focused on the practical step by step application of the CRISP -DM methodology, which contains real data from the field of mobile communications. Data mining task used in practical part is the behavioral prediction of mobile carrier customers. Supporting the practical part of the thesis, IBM SPSS Modeler was used as a main software for knowledge mining. Key words: data mining, knowledge disvocery in databases. Churm management, prediction, CRISP-DM.

This diploma thesis examines applications of datamining methods to open data. It is realized by solving analytical questions using the LISp-Miner system. Analytical questions are examined in data from The Czech Trade Inspection Authority from the perspective of the data owner. Procedure used to solve analytical questions is 4ft-Miner. There are presented and resolved four analytical questions, which are the results of the work. Work includes a detailed description of the transformation of the relational database into a format suitable for data mining. A detailed description of the data is also included. The theoretical part deals with the GUHA method and CRISP-DM methodology.

Throughout last decades, knowledge discovery from databases as one of the information and communicaiton technologies' disciplines has developed into its current state being showed increasing interest not only by major business corporates. Presented diploma thesis deals with problematique of data mining while paying prime attention to its practical utilization within business environment. Thesis objective is to review possibilities of data mining applications and to decompose implementation techniques focusing on specific data mining methods and algorithms as well as adaptation of business processes. This objective is subject of theoretical part of thesis focusing on principles of data mining, knowledge discovery from databases process, data mining commonly used methods and algorithms and finally tasks typically implemented in this domain. Further objective consists in presenting data mining benefits on the model example that is being displayed in the practical part of the thesis. Besides created data mining models evalution, practical part contains also design of subsequent steps that would enable higher efficiency in some specific areas of given business. I believe previous point together with characterization of knowledge discovery in databases process to be considered as the most beneficial one's of the thesis.

The aim of the thesis was to study and describe data mining methodology CRISP-DM. From the collected database of calls to the call center a prediction was performed, based on CRISP-DM methodology. In phase of test situation modeling four different testing methods were used: the k-NN, neural network, linear regression and super vector machine. The input attributes importance for further prediction was evaluated based on different selections. The results and findings may provide data for further more accurate forecasts in the future; not only in number of calls but also other indicators relevant to the call center.

This master’s thesis will look into the use of data mining in the area of segmentation and the prediction of onboarding candidates of a recruitment agency. The obtained results should serve to make company processes more effective concerning the processing of orders, and should also facilitate a more personal approach to candidates. The first chapter includes imperetive theoretical bases from the studies of Business Intelligence, data warehouses, data mining and marketing. Thereafter an analysis of the current state is presented with a focus on the capture of the key processes in processing and order. The last chapter looks at the proposed solution and implementation on the platform Microsoft SQL Server 2014. To conclude there are proposals of utilizing data mining in direct marketing.