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Knowledge Discovery over Data Warehouses
Pumprla, Ondřej ; Chmelař, Petr (referee) ; Stryka, Lukáš (advisor)
This Master's thesis deals with the principles of the data mining process, especially with the mining of association rules. The theoretical apparatus of general description and principles of the data warehouse creation is set. On the basis of this theoretical knowledge, the application for the association rules mining is implemented. The application requires the data in the transactional form or the multidimensional data organized in the Star schema. The implemented algorithms for finding of the frequent patterns are Apriori and FP-tree. The system allows the variant setting of parameters for mining process. Also, the validation tests and efficiency proofs were accomplished. From the point of view of the association rules searching support, the resultant application is more applicable and robust than the existing compared systems SAS Miner and Oracle Data Miner.
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Data Mining in Oracle Server Environment
Pap, Juraj ; Kunc, Michael (referee) ; Zendulka, Jaroslav (advisor)
The goal of this thesis was to deal with the problem of data mining and finding out support in DB server Oracle subsequently. The project is aimed at mining of association rules. The result of this work is an application that an user can use for working with mining models, changing their options and using an GUI to show the mined association rules. The application uses the support of DB server Oracle.
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Knowledge Data Discovery
Jaroš, Ondřej ; Stryka, Lukáš (referee) ; Jurka, Pavel (advisor)
This Bachelor work describes knowledge data discovery. There are in detail described methods of data mining in this text. This work is focusing principally on Associative rules method of knowledge data discovery, applying Apriory algorithm. The purpose of this work was to implement chosen method and verify its functionality on particular sample of data. Application is implemented in Java programming language and the sample of data, used for our method, is saved in XML file. Application doesnt work with classic database, but with XML file. Information needed to run the program is loaded from input (system) XML document. Discovered data are saved to system XML document.
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Big Social Data and the Study of Celebrity Fandom
Sedláček, Jakub ; Numerato, Dino (advisor) ; Špaček, Ondřej (referee) ; Mikuláš, Peter (referee)
This thesis provides a novel view of celebrity fandom through the lens of big social data, while at the same time exploring the opportunities and challenges of using digital traces from social media for sociological research. The first chapter provides a sociological framing of celebrity, a short, joint history of celebrity and the media, and a discussion of the revolutionary role that social media and its platforms played in celebrity culture. Finally, it attempts to bridge theories related to celebrity's role in society with research on "lifestyle politics", "polarization", "taste cultures" and "lifestyle enclaves". The second chapter serves as an introduction into big social data and digital trace data. First as a socio-technological phenomenon, then as a research tool. It covers its historical and current availability and discusses its epistemological and practical opportunities, limits and dangers. Finally, it introduces Facebook pagelikes as a valuable source of information on lifestyle politics. Chapter three is an exploration of Facebook digital traces of 90k celebrity followers. It asks whether celebrity preferences are related to differences in various aspects of life, including politics, leisure or cultural consumption. Methodologically, it covers combining data from APIs with web scraping and...
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Mining Multiple Level Association Rules
Nguyenová, Thanh Lam ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with multiple level association rules mining. The aim of this work is to focus on available algorithms for mining multiple level association rules and to implement an application with a graphical user interface that will demonstrate the functionality of these algorithms. Five algorithms based on the Apriori algorithm were chosen. Experiments with each algorithm were performed using the application and the results were compared and evaluated at the end of the thesis.
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Algorithm for Product Recommendation
Bodeček, Miroslav ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
The goal of this project is to explore the problem of product recommendations in the area of e-commerce and to evaluate known techniques, design product recommendation system for an existing e-commerce site, implement it and test it. This report introduces the problem, briefly examines current state of affairs in this area and defines requirements for a product recommendation module. The concept of data mining in general is introduced. The report proceeds to present detailed design corresponding to defined requirements and summarizes data gathered during testing phase. It concludes with evaluation and with discussion of the remaining goals for this thesis.
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Integration of Business Intelligence Tools into IS
Novák, Josef ; Bartík, Vladimír (referee) ; Stryka, Lukáš (advisor)
This Master's Thesis deals with the integration of Business Intelligence tools into an information system. There are concepts of BI, data warehouses, the OLAP analysis introduced as well as the knowledge discovery from databases, especially the association rule mining. In the chapters focused on practical part of the thesis, the design and implementation of resultant application are depicted. There are also the applied technologies like i.e. Microsoft SQL Server 2005 described.
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Data Mining in Small Business
Sabovčik, František ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
Tato práce si klade za cíl vyhodnotit techniky získávání znalostí pro využití v prostředí malého podnikání. Po prozkoumání dat a konzultace s doménovymi experty byly vybrány dvě úlohy: analyza nákupního košíku a predikce prodejů. Pro analyzu nákupního košíku byl využit algoritmus Relim pro vyhledávání častych itemsetů a metriky určující zajímavost asociačních pravidel. Pro úlohu predikce prodejů byl implementován dekompoziční model, SARIMA, MARS a neuronové sítě s časovym oknem. Modely byly vyhodnoceny. Pomocí optimalizace hyper-parametrů bylo dosaženo přijatelnych vysledků. Oproti předpokladům nedošlo při dodání dat o počasí a využití nelineárních modelů ke zlepšení oproti SARIMA. Predikce byla implementována jako služba na straně serveru pro testování v produkčním prostředí.
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OLAP Recommender
Koukal, Bohuslav ; Chudán, David (advisor) ; Vojíř, Stanislav (referee)
Manual data exploration in data cubes and searching for potentially interesting and useful information starts to be time-consuming and ineffective from certain volume of the data. In my thesis, I designed, implemented and tested a system, automating the data cube exploration and offering potentially interesting views on OLAP data to the end user. The system is based on integration of two data analytics methods - OLAP analysis data visualisation and data mining, represented by GUHA association rules mining. Another contribution of my work is a research of possibilities how to solve differences between OLAP analysis and association rule mining. Implemented solutions of the differences include data discretization, dimensions commensurability, design of automatic data mining task algorithm based on the data structure and mapping definition between mined association rules and corresponding OLAP visualisation. The system was tested with real retail sales data and with EU structural funds data. The experiments proved that complementary usage of the association rule mining together with OLAP analysis identifies relationships in the data with higher success rate than the isolated use of both techniques.
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Data comparability in knowledge discovery in databases
Horáková, Linda ; Chudán, David (advisor) ; Svátek, Vojtěch (referee)
The master thesis is focused on analysis of data comparability and commensurability in datasets, which are used for obtaining knowledge using methods of data mining. Data comparability is one of aspects of data quality, which is crucial for correct and applicable results from data mining tasks. The aim of the theoretical part of the thesis is to briefly describe the field of knowledqe discovery and define specifics of mining of aggregated data. Moreover, the terms of comparability and commensurability is discussed. The main part is focused on process of knowledge discovery. These findings are applied in practical part of the thesis. The main goal of this part is to define general methodology, which can be used for discovery of potential problems of data comparability in analyzed data. This methodology is based on analysis of real dataset containing daily sales of products. In conclusion, the methodology is applied on data from the field of public budgets.
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