Photo credit: Anna Logue

Data Mining and Matrices (FSS 2019)


  • Lecturer: Prof. Dr. Rainer Gemulla
  • Tutor: Daniel Ruffinelli
  • Type of course: Lecture and practical exercises (6 ECTS points)
  • Lecture: Thursday, 10:15-11:45, Room A5 6, C015; starts Feb 14 (1st week)
  • Tutorium: Tuesday, 08:30-10:00, Room B6 24-26, A101; starts Feb 19 (2nd week)
  • Evaluation: Final exam or oral examination, assignments
  • Prerequisites: Data Mining I (strongly recommended), programming experience
  • 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.

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

In Ilias.


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