Web Data Mining

Course guarantor:

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, 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. Key web data mining principles.

2. Web content mining approaches (formats, restrictions, ethical aspects).

3. Web content mining tools.

4. Accessing and extracting specific web content (deep web).

5. Main text mining concepts.

6. Practical applications of text mining.

7. (2) Social network structure and content analysis.

8. Web graph, web structure mining.

9. Web usage mining: data collecting.

10. Web usage mining: data analysis, web analytics.

11. Recommender systems and personalization.

12. Data stream mining: algorithms and applications.

Syllabus of tutorials:

1. Basics of data acquisition and processing

2. Text preprocessing, text mining applications

3. Acquisition and analysis of graph-based data

4. User data analysis

5. Basics of recommendation systems

6. Project presentation and assessment

Study Objective:

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, 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.

Study materials:

1. Liu, B.: Web Data Mining. Springer, 2011. ISBN 978-3-642-19459-7.

2. Charu C. Aggarwal: Machine Learning for Text. Springer, 2018. ISBN 9783319735313.

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

4. Russel,A. M.: Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (3rd Edition). OReilly Media, 2019. ISBN 978-1491985045.

5. Charu C. Aggarwal: Recommender Systems: The Textbook. Springer, 2016. ISBN 9783319296579.

Note:

Further information:

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

No time-table has been prepared for this course

The course is a part of the following study plans:

• Quantum Informatics (elective course)
• Mgr. programe Applied informatics (code ANIE) for the phase of study without specialization (VO)
• Master specialization Embedded systems (VO)
• Master specialization Business Informatics, 2026 (VO, compulsory elective course)
• Master specialization Software Engineering (VO)
• Master specialization Web Engineering (PS)
• Master specialization Visual computing and Game design (VO)
• Master specialization Computer Security, in Czech, 2026 (elective course)
• Master specialization Computer Systems and Networks, in Czech, 2026 (elective course)
• Master specialization Computer Science, in Czech, 2026 (elective course)
• Master specialization Programming Languages, in Czech, 2026 (elective course)
• Master specialization Artificial Intelligence, in Czech, 2026 (elective course)
• Master programme, for the phase of study without specialisation, ver. for 2026 and higher (elective course)