Web Data Mining

Garant předmětu:

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:

https://courses.fit.cvut.cz/MI-DDW/

No time-table has been prepared for this course

The course is a part of the following study plans:

• Master branch Knowledge Engineering, in Czech, 2016-2017 (compulsory course of the specialization)
• Master branch Computer Security, in Czech, 2016-2019 (elective course)
• Master branch Computer Systems and Networks, in Czech, 2016-2019 (elective course)
• Master branch Design and Programming of Embedded Systems, in Czech, 2016-2019 (elective course)
• Master branch Web and Software Engineering, spec. Info. Systems and Management, in Czech, 2016-2019 (elective course)
• Master branch Web and Software Engineering, spec. Software Engineering, in Czech, 2016-2019 (elective course)
• Master branch Web and Software Engineering, spec. Web Engineering, in Czech, 2016-2019 (compulsory course of the branch)
• Master program Informatics, unspecified branch, in Czech, version 2016-2019 (VO)
• Master branch System Programming, spec. System Programming, in Czech, 2016-2019 (elective course)
• Master branch System Programming, spec. Computer Science, in Czech, 2016-2017 (elective course)
• Master specialization Computer Science, in Czech, 2018-2019 (elective course)
• Master branch Knowledge Engineering, in Czech, 2018-2019 (compulsory course of the specialization)