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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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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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Data Mining in K2 Information System
Figura, Petr ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This project was originated by K2 atmitec Brno s.r.o. company. The result is data mining module in K2 information system environment. Engineered data module implements association analysis over the data of K2 information system data warehouse. Analyzed data contains information about sales filed in K2 information system. Module is implementing consumer basket analysis.
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Using data mining to manage an enterprise.
Prášil, Zdeněk ; Pour, Jan (advisor) ; Novotný, Ota (referee)
The thesis is focused on data mining and its use in management of an enterprise. The thesis is structured into theoretical and practical part. Aim of the theoretical part was to find out: 1/ the most used methods of the data mining, 2/ typical application areas, 3/ typical problems solved in the application areas. Aim of the practical part was: 1/ to demonstrate use of the data mining in small Czech e-shop for understanding of the structure of the sale data, 2/ to demonstrate, how the data mining analysis can help to increase marketing results. In my analyses of the literature data I found decision trees, linear and logistic regression, neural network, segmentation methods and association rules are the most used methods of the data mining analysis. CRM and marketing, financial institutions, insurance and telecommunication companies, retail trade and production are the application areas using the data mining the most. The specific tasks of the data mining focus on relationships between marketing sales and customers to make better business. In the analysis of the e-shop data I revealed the types of goods which are buying together. Based on this fact I proposed that the strategy supporting this type of shopping is crucial for the business success. As a conclusion I proved the data mining is methods appropriate also for the small e-shop and have capacity to improve its marketing strategy.
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