Data mining and knowledge discovery

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Data mining and knowledge discovery

Code: 255123
ECTS: 5.0
Lecturers in charge: doc. dr. sc. Matej Mihelčić
Take exam: Studomat
Load:

1. komponenta

Lecture typeTotal
Lectures 45
* Load is given in academic hour (1 academic hour = 45 minutes)
Description:
Literature:
  1. Data mining and knowledge discovery handbook, Lior Rokach, Springer, New York, 2005.
  2. Foundations of Rule Learning, Johannes Furnkranz, Dragan Gamberger, Nada Lavrač, Springer Science & Business Media, 2012.
  3. Redescription mining, Esther Galbrun, Pauli Miettinen, Springer, Cham, 2017.
  4. Data mining: the textbook, Aggarwal, Springer, New York, 2015.
  5. Journal of Machine learning research, Nada Lavrač et al. Subgroup Discovery with CN2-SD, 2004.
  6. Artificial Intelligence Reviews, A review of conceptual clustering algorithms, 2019.
1. semester Not active
Izborni predmet 1, 2, 3 - Regular study - Mathematical Statistics

2. semester
Izborni predmet 1, 2, 3 - Regular study - Mathematical Statistics

3. semester Not active
Izborni predmet 4, 5, 6, 7 - Regular study - Mathematical Statistics

4. semester
Izborni predmet 4, 5, 6, 7 - Regular study - Mathematical Statistics
Consultations schedule:
  • doc. dr. sc. Matej Mihelčić:

    Software engineering: Thursday, 15h-17h

    Applied object oriented programming: Tuesday, 15h-17h

    Computer networks: Wednesday, 10h-12h

    Mandatory notification via e-mail in advance!

    Location: 226