Course Description

NOTE: This lecture will NOT be offered in FSS 2019, since the module leader (Simone Ponzetto) is on sabbatical.

The vast amounts of textual content and structured data found on the Web provide us with a goldmine of data that can be mined to derive knowledge about nearly any aspect of human life. The course covers advanced data mining techniques for extracting knowledge from Web content as a basis for business decisions and applications. The course will cover, among others, the following topics:

  • Web Usage Mining
  • Web Structure Mining
  • Web Content Mining

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. As part of the projects, students develop solutions for a variety of web mining problems and report on the results of their projects in the form of a written report as well as an oral presentation.

Time and Location

  • Start: 13.2.2018
  • Tuesday, 10:15 to 11:45, Room: B 6, A104
  • Tuesday, 15:30 to 17:00, Room: B 6, A104

Instructor

Final mark

  • 50 % written exam
  • 50 % project work

Slides and Excercises

  1. Slideset: Introduction and Course Outline

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 opens on February 5, 2018 at 17:30.
  • Places are assigned on first come/first serve basis.
  • Students register for the course by joining the ILIAS group.

Requirements

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

 Course Evaluations

 Outline 

WeekMorning lecture (10:15-11:45)Afternoon lecture (15:30-17:00)
13.02.2018Introduction and Course Outline -
20.02.2018Web Usage MiningWeb Usage Mining
27.02.2018Web Usage MiningWeb Content Mining
6.03.2018Web Content MiningWeb Content Mining
13.03.2018Web Structure MiningWeb Structure Mining
20.03.2018 -Introduction to the student projects
- Easter break -
10.04.2018 -Feedback on the projects outline
17.04.2018Project workProject work
24.04.2018Project workCoaching
1.05.2018- Holiday - -
8.05.2018Project workCoaching
15.05.2018Project workCoaching
22.05.2018Project presentationsProject presentations
29.05.2018Final exam -

 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