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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Web Data Mining

The course is not on the list Without time-table
Code Completion Credits Range Language
MI-DDW.16 Z,ZK 5 2P+1C Czech
Lecturer:
Tutor:
Supervisor:
Department of Software Engineering
Synopsis:

Students will learn latest methods and technologies for Web data acquisition, analysis and utilization of the discovered knowledge. Students will gain an overview of Web mining techniques for Web crawling and search, Web structure analysis, Web usage analysis, Web content mining and information extraction. Students will also gain an overview of most recent developments in the field of social web and recommendation systems.

Requirements:

Basic knowledge in Web architecture (HTTP, HTML, URI), programming skills (e.g. Java, JavaScript), graph theory and basic algorithms.

Syllabus of lectures:

1. Motivation and Course Overview

2. Data Access and Acquisition Methods

3. Indexing and Document Retrieval on the Web

4. Text Mining

5. Applications of Text Mining

6. Social Network Analysis

7. Page Rank and HITS

8. Mining Social Web

9. Web Analytics

10. (2) Mining Data Streams

11. (2) Recommender Systems

Syllabus of tutorials:

1. Basics of data acquisition and processing

2. Text preprocessing, text mining applications

3. Project presentation, consultations

4. User data analysis

5. Basics of recommendation systems

6. Project presentation and assessment

Study Objective:

Provide students with an overview of web mining technologies and qualify them to use some of them in practice.

Study materials:

1. Liu, B. „Web Data Mining“, Springer-Verlag Berlin Heidelberg, 2011. ISBN 978-3-642-19459-7.

2. Easley, D., Kleinberg, J. „Networks, Crowds, and Markets: Reasoning About a Highly Connected World“, Cambridge University Press, 2010. ISBN 978-0521195331.

3. Ricci, F., Rokach, L., Shapira, B., B. Kantor, P. „Recommender Systems Handbook“, Springer, 2010. ISBN 978-0387858197.

4. Kaushik, A. „Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity“, Sybex, 2009. ISBN 978-0470529393.

5. Marmanis, H., Babenko, D. „Algorithms of the Intelligent Web“, Manning Publications, 2009. ISBN 978-1933988665.

6. A. Russel, M. „Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More“, O'Reilly Media, 2013. ISBN 978-1449367619.

7. Chakrabarti, S. „Mining the Web: Discovering Knowledge from Hypertext Data“, Morgan Kaufmann, 2002. ISBN 1558607544.

Note:
Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2019-10-18
For updated information see http://bilakniha.cvut.cz/en/predmet4651506.html