P056 Knowledge Discovery in Databases

Faculty of Informatics
Spring 2000
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)
Guaranteed by
prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.
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.
  • Basci algorithms of machine learning.
  • 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.
The course is taught: every week.
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 2001, Spring 2002.
  • Enrolment Statistics (Spring 2000, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2000/P056