FI:PV056 Knowledge discovery in DB

PV056 Knowledge Discovery in Databases

Faculty of Informatics
Spring 2003

Extent and Intensity

2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)

Guaranteed by

Timetable

Mon 14:00–15:50 B011

Prerequisites (in Czech)

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

there are 8 fields of study the course is directly associated with, display

Course objectives (in Czech)

Přehled základních metod, algoritmů a systémů pro vyhledávání znalostí v databázích. Součástí předmětu je projekt.

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. xiv, 611. ISBN 0262560976. 1996. info

Assessment methods (in Czech)

Nutnou podmínkou absolvování je projekt.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

Teacher's information

http://www.fi.muni.cz/usr/popelinsky/lectures/kdd/