Lectures

• Lecture 1 (6.9) - Introduction
  • Slides: Slides (pdf) (pdf 6up)
• Lecture 2 (13.9, 20.9) - Frequent Itemset Mining & Association rules
  • Slides: Slides (pdf) (pdf 6up)
  • Textbook 1: http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf
  • Textbook 2: http://i.stanford.edu/~ullman/mmds/ch6a.pdf
• Lecture 3 (27.9, 11.10, 18.10) - Clustering and seriation
  • Slides: Slides (pdf) (pdf 6up)
  • Slides: (pdf 6up) (slides by I. Liiv)
• Lecture 4 (25.10) - Descriptive data analysis, Data cleansing
  • Slides: Slides (pdf) (pdf 6up)
• Lecture 5 (1.11, 8.11) - OLAP, Multidimensional modeling, data cubes
  • Slides: Slides (pdf) (pdf 6up)
• Lecture 6 (15.11, 22.11) - Machine Learning
  • Slides: Slides (pdf) (pdf 6up)
  • Domingos: A few useful things to know about machine learning. Communications of the ACM, Vol. 55 No. 10, Pages 78-87 doi: 10.1145/2347736.2347755 (via ACM Digital library, Attach:domingos.pdf )
• Lecture 7 (29.11) - Text Mining
  • Slides: Slides (pdf) (pdf 6up)
• Lecture 8 (06.12) - Stream Mining, Episodes, Pattern Discovery, Web, SNA
  • Slides: Slides (pdf) (pdf 6up)