IE 673: Data Mining and Matrices (FSS 2017)


  • DMM lecture & tutorial moved to room B6, A301


  • Lecturer: Prof. Dr. Rainer Gemulla
  • Tutor: Yanjie Wang
  • Type of course: Lecture and practical exercises (6 ECTS points)
  • Lecture: Wednesday, 08:30-10:00, Room A5 6, C014 Room B6, A301
  • Tutorium: Wednesday, 10:15-11:45, Room A5 6, C014 Room B6, A301
  • Evaluation: Final exam or oral examination, assignments
  • Prerequisites: Data Mining I (strongly recommended)
  • Registration: Please register in ILIAS to participate in the course.


Many data mining tasks operate on dyadic data, i.e., data involving two types of entities (e.g., users and products, objects and attributes, points and coordinates, or vertices in a graph). Such dyadic data can be naturally represented in terms of a matrix, which opens up a range of powerful data mining techniques. This course provides an introduction into matrix decomposition models and algorithms for analyzing dyadic data, covers data mining tasks such as prediction, clustering, pattern mining, and dimensionality reduction, as well as application areas such as recommender systems, information retrieval, information extraction, and topic modelling.

Data Matrix Mining
Book 1 5 0 3
Book 2 0 0 7
Book 3 4 6 5
Avatar The Matrix Up
Alice 4 2
Bob 3 2
Charlie 5 3
A document–term matrix            An incomplete rating matrix
Hot Topics
in IR
IR &
DM &
Student A 1 1 0
Student B 1 1 1
Student C 0 1 1
Jan. June Sept.
Saarbrücken –1 11 10
Helsinki –6.5 10.9 8.7
Cape Town 15.7 7.8 8.7
A student–course matrix            Cities and their average minimum temperatures

List of topics (tentative):
  • Singular value decomposition (SVD)
  • Non-negative matrix factorization (NMF)
  • Boolean matrix decomposition (BMF)
  • Independent component analysis (ICA)
  • Matrix completion
  • Probabilistic matrix factorization
  • Spectral clustering
  • Label propagation
  • Graph analysis
  • Tensors

Course materials

Organization; all other material in Ilias.


  • David Skillicorn
    Understanding Complex Datasets: Data Mining with Matrix Decompositions
    Chapman & Hall, 2007
  • See lecture notes for additional references.