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:
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/
|Week||Morning lecture (10:15-11:45)||Afternoon lecture (15:30-17:00)|
|13.02.2018||Introduction and Course Outline||-|
|20.02.2018||Web Usage Mining||Web Usage Mining|
|27.02.2018||Web Usage Mining||Web Content Mining|
|6.03.2018||Web Content Mining||Web Content Mining|
|13.03.2018||Web Structure Mining||Web Structure Mining|
|20.03.2018||-||Introduction to the student projects|
|- Easter break -|
|10.04.2018||-||Feedback on the projects outline|
|17.04.2018||Project work||Project work|
|1.05.2018||- Holiday -||-|
|22.05.2018||Project presentations||Project presentations|
Bing Liu: Web Data Mining, 2nd Edition, Springer.
Wouter de Nooy, Andrej Mrvar, Vladimir Batagelj: Exploratory Social Network Analysis with Pajek, Cambridge University Press.
Dietmar Jannach: Recommender Systems: An Introduction, Cambridge University Press.
Pang-Ning Tan, Michael Steinback, Vipin Kumar: Introduction to Data Mining, Pearson.
Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.