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Data Mining
Stehno, David ; Hynčica, Tomáš (referee) ; Honzík, Petr (advisor)
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.
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Utilization of Data Mining for Personnel Agency
Ondruš, Erik ; Janata, Michal (referee) ; Luhan, Jan (advisor)
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.
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Data Analysis of a Company Producing Medical Supplies
Kulhánková, Monika ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
This bachelor's thesis deals with the analysis of the company's sales data, specifically the classification of the customer's type according to his sales data. It provides a theoretical introduction to data mining. It describes the classification process and methods for creating classifiers and presents the CRISP-DM model. This thesis describes the provided data sets, from which the relevant attributes are selected. The data are preprocessed and used in the creation and testing of classification models. The result of this thesis is a comparison of the achieved results.
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Web Application for Managing and Classifying Information from Distributed Sources
Vrána, Pavel ; Chmelař, Petr (referee) ; Drozd, Michal (advisor)
This master's thesis deals with data mining techniques and classification of the data into specified categories. The goal of this thesis is to implement a web portal for administration and classification of data from distributed sources. To achieve the goal, it is necessary to test different methods and find the most appropriate one for web articles classification. From the results obtained, there will be developed an automated application for downloading and classification of data from different sources, which would ultimately be able to substitute a user, who would process all the tasks manually.
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Using Data Mining in Various Industries
Fabian, Jaroslav ; Novotný, Jakub (referee) ; Kříž, Jiří (advisor)
This master’s thesis concerns about the use of data mining techniques in banking, insurance and shopping centres industries. The thesis theoretically describes algorithms and methodology CRISP-DM dedicated to data mining processes. With usage of theoretical knowledge and methods, the thesis suggests possible solution for various industries within business intelligence processes.
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Data Mining
Slezák, Milan ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
The thesis is focused on an introduction of data mining. Data mining is focused on finding of a hidden data correlation. Interest in this area is dated back to the 60th the 20th century. Data analysis was first used in marketing. However, later it expanded to more areas, and some of its options are still unused. One of methodologies is useful used for creating of this process. Methodology offers a concise guide on how you can create a data mining procedure. The data mining analysis contains a wide range of algorithms for data modification. The interest in data mining causes that number of data mining software is increasing. This thesis contains overviews some of this programs, some examples and assessment.
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Data Analysis of a Company Producing Medical Supplies
Kulhánková, Monika ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
This bachelor's thesis deals with the analysis of the company's sales data, specifically the classification of the customer's type according to his sales data. It provides a theoretical introduction to data mining. It describes the classification process and methods for creating classifiers and presents the CRISP-DM model. This thesis describes the provided data sets, from which the relevant attributes are selected. The data are preprocessed and used in the creation and testing of classification models. The result of this thesis is a comparison of the achieved results.
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Visualization of data mining results through BI tools
Fiřtík, Zdeněk ; Chudán, David (advisor) ; Rauch, Jan (referee)
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.
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Valuation of real estates using statistical methods
Funiok, Ondřej ; Pecáková, Iva (advisor) ; Řezanková, Hana (referee)
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.
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Using system LISp-Miner for large real data
Hrnčíř, Jan ; Rauch, Jan (advisor) ; Chudán, David (referee)
This dissertation thesis describes an advanced method of knowledge discovery in databases (KDD), implemented in system LISp-Miner. The goal is to show the possibilities of coordinated use of analytical tools and complex procedures GUHA in this system. The thesis uses methodology CRISP-DM, which is firstly described and work is proceeded using this methodology in the following sections. The author firstly introduces readers domain area and then the data itself, which are processed to the analysis needs. Analytical questions that are answered at, are drawn from the literature, which is focused on domain area. The work should be used as a guide to LISp-Miner users, using analytical tools and procedures GUHA is therefore described the easiest way to understand.
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