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Data Mining and Matrices (FSS 2018)

Organization

  • 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, C015
  • Tutorium: Thursday, 10:15-11:45, Room A5 6, C015
  • 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.

Content

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.

  • Literature

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