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The aim of this paper is to describe and illustrate benefits of using Data Mining for effective customer communication. The objective is to perform a Data Mining analysis in order to achieve results with potentially beneficial influence on the company s relationship with its customers, while using the KNIME Analytics Platform tool. The paper introduces the theoretical aspect of Customer Relationship Management, Data Mining and the opportunities of using Data Mining to improve CRM; followed by a market analysis of available Data Mining tools and the introduction of the KNIME Analysis Platform. Furthermore, the knowledge thus reached is used for the performance of real data analysis with the aim of reaching customer knowledge that would be appropriate to use within CRM strategy and finally to positively influence the value of customer relationships.

V příspěvku provádíme základní analýzu účinné právní úpravy v oblasti analýzy (textového a data miningu). Po stručném představení základních principů fungování datové analýzy je pozornost věnována výjimce pro vědecké účely (čl. 5 odst. 3 InfoSocS a čl. 9 písm. b) DataS) a její aplikovatelnosti na tento způsob získávání nových informací, poznatků a znalostí. Referenčním rámcem je pak primárně unijní úprava v příslušných směrnicích a jejich národní implementace. This paper conducts a basic analysis of the legal norms governing data analysis (text and data mining). After the brief introduction of the basic principles of data analysis, this paper focuses on the exemption for the research purposes (art. 5 par. 3 letter a InfoSocDir and art. 9 letter b) DataDir) and its applicability on this mean of obtaining new information and knowledge. The primary framework of reference is the legal framework provided by the European Union in directives and national implementations.

Data mining is a new discipline lying at the interface of statistics, database technology, pattern recognition, machine learning, and other areas. It is concerned with the secondary analysis of lar- ge databases in order to find previously unsuspected relationships which are of interest or value to the database owners. There are two keys to success in data mining. First is coming up with a precise formulation of the problem you are trying to solve. A focused statement usually results in the best payoff. The second key is using the right data. After choosing from the data available to you, or perhaps buying external data, you may need to transform and combine it in significant ways. New problems arise, partly as a consequence of the sheer size of the data sets involved, and partly because of issues of pattern matching. H owever, since statistics provides the intellectual glue underlying the effort, it is important for statisticians to become involved. There are very real opportunities for statisticians to make significant contributions. The main definition of data mining and the special data mining tasks are mentioned in the first part of this paper. The data mining problem was also discussed in previous issues of E+M. One method (clustering) was chosen to be a subject of this article. One of the opportunities to gain knowledge from data is a use of clustering analysis. Clustering analysis belongs to unsupervised methods of data mining. We put here a focus on this method. Some basic principles are described in the second part of this paper. This method is examined on two examples from the marketing field. In the first example is used software Statgraphics 5.0Plus (www.statgraphics.com) to solve clustering problem (nearest neighbour algorithm and Eucleidi- an distance), and in the second example is used Statistica 6.0Cz software (from Statoft, Inc., www.statsoft.com or www.statsoft.cz). But the building models is only one step in knowledge discovery. It is vital to properly collect and prepare the data, and to check the models against the real world. The „best“ model is often found after building models of several different types, or by trying different technologies or algorithms.

This bachelor thesis deals with the area of NoSQL databases with a focus on graph databases and the application of data mining methods. The main aim is to install a specific NoSQL graph database Neo4j together with the characterization of its key features and activities and to use data mining methods above the graph database. Methods of data mining are performed and analyzed the most typical examples of data obtained from the Internet. The used query language is Cypher. The last part of this thesis is an analysis of selected data from Neo4j Sandbox with a focus on the Panama Papers in the Neo4j database. To present this example, tools as Neo4j Community Server and Cypher language are used. The results of this analysis are then evaluated for their possible further use.

Akce Crashday 2016 byla vyvrcholením několikaletých příprav Ústavu soudního inženýrství Vysokého učení technického v Brně, jejichž cílem bylo vyvinout a připravit vybavení pro realizaci a sběr dat při nárazových zkouškách vozidel. Pro tuto akci se podařilo zajistit ve spolupráci se společností Škoda Auto vozidla Škoda Superb a Škoda Fabia roku výroby 2015. Obsahem tohoto příspěvku je představení použitého speciálně vyvinutého vybavení a prezentace výsledků čtyř nárazových zkoušek, využitelných ve znalecké analýze. The Institute of Forensic Engineering of the Technical University in Brno has in terms of event called Crashday 2016 finished efforts which took several years of preparation for crash test data collection. The goal of the institute’s effort was to develop and prepare equipment for dynamical data collection during crash tests. With the help of cooperation with Skoda manufacturer the vehicles of Skoda Superb and Skoda Fabia (production year 2015) have been ensured. The content of this paper aims at presentation of a specially developed equipment and presentation of the four crash tests results.

The topis of this thesis is an attempt to visualize the data of the minig data through the tools of business intelligence. The paper moves from a detailed characterization of basic concepts, methodologies and procedures, which are an integral part of the data mining process and subsequent visualization. The thesis is meant for those who are interested in data mining and BI tools, and can offer motives to consider whether these tools will be suitable for their use.

Data mining Bc. thesis Jakub Dolejšek (english abstract) This paper describes problematic of the knowleadge database discovery with focus on methods of decision trees and neural networks with examples of their application on concrete examples. Powered by TCPDF (www.tcpdf.org)

This paper is about application of various data mining methods in social networks and social media area. It reveals basic principles of social media with the aim to high information potential of usage of the data from social networks. This is demonstrated on selected data mining methods, especially Social Network Analysis and Sentiment Analysis. Other opportunities of using social media data are shown in chapter about Social Media Monitoring tools. All these chapters are supplemented by practical examples and particular researches. Last chapter reveals visions and threats, which can bring data mining in the future. Keywords Data mining, social networks, social media, social network analysis, sentiment analysis, social media monitoring

This thesis analyzes various factors of impact on current data warehouse solutions. It is structured along three main sections. The first section dissects current issues faced by data warehouses. The second section focuses on an analysis of how the market for data warehouse solutions has developed; within this context, it also mentions other, related markets. The last section is devoted to current trends in the area of data warehouses and Business Intelligence. While this work focuses on data warehouses proper, the topic is closely interconnected with the overarching category of Business Intelligence, which is why a suitable degree of discussion also of this area appeared to be in order. This paper does not seek to provide advice as to which specific solutions management should choose for their business, nor to serve as a manual on how exactly to implement a data warehouse so as to avoid potential issues. Rather, this thesis attempts to provide a comprehensive and transparent overview of the factors which have impact on today's data warehouse solutions. The rationale behind this thesis is to draw special attention to the key influences on data warehouse solutions at this point in time and to give an informed estimate of their likely future development.

One of the oppotunities in data mining is a use of clustering analysis. Clustering analysis belongs to unsupervised methods of data mining. We put here a focus on this method. Some basic principles are described in the second part of this paper. This method is examined on two examples from the marketing field. In the first example is used software Statgraphics 5.0Plus to solve clustering problem (nearest neighbour algorithm and Eucleidian distance); and in the second example is used Statistica 6.0Cz software (from Statoft; Inc.).

The data structure estimation may provide a connection between the semantic web vision and currently used web sources containing no semantic description of presented information. It enables information from web pages to be processed by semantic web tools with the best to be known about these sources, with the source estimated data structure. The paper gives a method description and presents its usage for a inductive logic programming task. The advantages of a rule-based system usage is given as well.