Web Mining (FSS2013)

The textual content as well as the structured data which is accessible on the Web has an enormous potential for being mined to derive knowledge about nearly any aspect of human life.  The course covers advanced data mining techniques for extracting knowledge from Web content, the Web link structure, as well as usage data gathered by Web applications.  The course will cover the following topics: 

  • Web Usage Mining
  • Recommender Systems
  • Web Structure Mining
  • Social Network Analysis
  • Web Content Mining
  • Information Extraction
  • Sentiment Analysis

The course consists of a lecture together with accompanying practical exercises as well as student team projects.

In the exercises the participants will gather initial expertise in applying state of the art web mining tools/libraries on realistic data sets.

The team projects take place in the last third of the term. Within the projects, students realize more sophisticated web mining projects of personal choice and report about the results of their projects in the form of a written report as well as an oral presentation.

Time and Location

  • The course will be held the next time in FSS2014.

Instructor

  • Prof. Dr. Christian Bizer
  • Cäcilia Zirn
  • Robert Meusel

Final mark

  • 50 % written exam
  • 50 % project work

Exam Review

  • Wednesday, 25.09.2013, 15:00-15:30, Room: B6 26, A2.06

Slides and Excercises

  • The lecture slides and excercises are provided in ILIAS.

Participation 

  • The course is open to students of the Master Business Informatics 
  • The course is restricted to 30 participants
  • Students can register by joining the ILIAS group

Requirements

  • The lecture can be attended without having attended the BI 600 Data Mining lecture before
  • Basic programming skills in Java are required for the exercise

 Course Evaluations

 Outline

Week

Topic Tuesday

Topic Thursday

12.2.2013

Lecture: Introduction to Web Mining

Lecture: Web Usage Mining

19.2.2013

Lecture: Recommender Systems

Exercise: Recommender Systems

26.2.2013

Exercise: Recommender Systems

Lecture: Web Structure Mining

5.3.2013

Lecture: Social Network Analysis

Lecture: Introduction to Pajek

12.3.2013

Exercise: Social Network Analysis

Exercise: Social Network Analysis

19.3.2013

Lecture: Web Content Mining

Lecture: Information Extraction and
Sentiment Analysis

9.4.2013

Exercise: Information Extraction and
Sentiment Analysis

Introduction to student projects

16.4.2013

Discussion of project outlines

Project work

23.4.2013

Coaching

Project work

30.4.2013

Coaching

Project work

7.5.2013

Coaching

Project work

14.5.2013

Coaching

Project work

21.5.2013

Coaching

Presentation of project results

28.5.2013

Presentation of project results

Presentation of project results

 Literature 

  1. Bing Liu: Web Data Mining, 2nd Edition, Springer.
  2. Wouter de Nooy, Andrej Mrvar, Vladimir Batagelj: Exploratory Social Network Analysis with Pajek, Cambridge University Press.
  3. Dietmar Jannach: Recommender Systems: An Introduction, Cambridge University Press.
  4. Pang-Ning Tan, Michael Steinback, Vipin Kumar: Introduction to Data Mining, Pearson.
  5. Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.

Software