P056 Knowledge Discovery in Databases

Faculty of Informatics
Spring 1999
Extent and Intensity
2/0. 2 credit(s). Recommended Type of Completion: k (colloquium). Other types of completion: z (credit).
Teacher(s)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Guaranteed by
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
  • Date warehousing, OLAP, ROLAP.
  • Knowledge, association, dependency in databases. Interestingness relation. Knowledge discovery in databases(KDD). Data mining.
  • Typical KDD tasks: clustering, classification, dependency discovery, deviation detection.
  • Introduction to knowledge discovery algorithms: Machine learning. Statistics.
  • DBMS extension to support KDD. KESO Project.
  • Inductive query languages. DBLearn.
  • Knowledge discovery in RDB, OODB, and WWW.
  • KDD systems: C4.5, CLEMENTINE, ECCE.
Language of instruction
Czech
Further comments (probably available only in Czech)
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 2000, Spring 2001, Spring 2002.
  • Enrolment Statistics (Spring 1999, recent)
  • Permalink: https://is.muni.cz/course/fi/spring1999/P056