Course Description

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 link structure of the Web, 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

  • Tuesday, 15:30 to 17:00, Room: B 6, A104, Start: 14.2.2017
  • Thursday, 15:30 to 17:00, Room: B 6, A104, Start: 16.2.2017

Instructor

Final mark

  • 50 % written exam
  • 50 % project work

Slides and Excercises

  1. Slideset: Introduction and Course Outline
  2. Slideset: Web Usage Mining
  3. Slideset: Web Structure Mining
  4. Slideset: Web Content Mining
  5. Slideset: Introduction to Student Projects

Lecture Videos

Participation 

  • The course is open to students of the Master Business Informatics and Mannheim Master in Data Science (MMDS). 
  • The course is restricted to 30 participants.
  • The registration for the course is opend on 8. February 2017 at 10:00.
  • Places are assigned on first come/first serve basis.
  • Students register for the course by joining this ILIAS group (once the registration is open).

Requirements

  • Basic programming skills in Java are required for the exercise.
  • It is beneficial to addend the course IE 500 Data Mining before this course.

 Course Evaluations

 Outline

Week

Topic Tuesday

Topic Thursday

14.02.2017

Lecture: Introduction to Web Mining

Lecture: Web Usage Mining

21.02.2017

Lecture: Recommender Systems

Exercise: Recommender Systems

28.02.2017

Exercise: Recommender Systems

Lecture: Web Structure Mining

07.03.2017

Lecture: Social Network Analysis

Exercise: Introduction to Pajek

14.03.2017

Exercise: Social Network Analysis

Exercise: Social Network Analysis

21.03.2017

Lecture: Web Content Mining: Sentiment analysis

Exercise:  Sentiment Analysis

28.03.2017 Lecture:  Web Content Mining: Information Extraction Exercise: Information Extraction
04.04.2017 Introduction to Student Projects Prepararation of Project Outlines
- Easter break -
25.04.2017 Feedback about Project Oulines Project work

02.05.2017

Project work

Coaching

09.05.2017

Project work

Coaching

16.05.2017

Project work

Coaching

23.05.2017

Project work

- Holiday -

30.05.2017 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