P056 Knowledge Discovery in Databases

Faculty of Informatics
Spring 2002
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Petr Kuba, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.
Timetable
Thu 12:00–14:50 B204
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Syllabus
  • Knowledge, association, dependency in databases. Interestingness relation. Knowledge discovery in databases(KDD). Data mining.
  • Typical KDD tasks: clustering, classification, dependency discovery, deviation detection.
  • Basic algorithms of machine learning.
  • Preprocessing.
  • Association rules
  • KDD systems MineSet and Kepler.
  • DBMS extension to support KDD. KESO Project.
  • Inductive query languages. DBLearn.
  • Knowledge discovery in RDB, OODB, geographic data and WWW and text.
  • Data warehousing, OLAP.
Literature
  • Advances in knowledge discovery and data mining. Edited by Usama M. Fayyad. Menlo Park: AAAI Press, 1996, xiv, 611. ISBN 0262560976. info
Assessment methods (in Czech)
Nutnou podmínkou absolvování je projekt.
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/usr/popelinsky/lectures/kdd/
The course is also listed under the following terms Spring 1997, Spring 1998, Spring 1999, Spring 2000, Spring 2001.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2002/P056