|
|
|
||
|
Advanced ways of dealing with knowledge including knowledge acquisition, formalization, integration, presentation of
knowledge to users as well as automated knowledge application in various areas. The goal of the course is to acquaint
students with new approaches to DM that use methods of knowledge engineering. The emphasis will be on student's
concrete projects.
Last update: T_KSI (28.04.2008)
|
|
||
|
Hájek P., Havránek T.: Mechanizing hypothesis formation (mathematical foundations for a general theory), Springer-Verlag Berlin-Heidelberg-New York, 1978
RAUCH, Jan. Logic of Association Rules. Applied Intelligence, 2005, č. 22, s. 9-28. .
RAUCH, Jan, ŠIMŮNEK, Milan. An Alternative Approach to Mining Association Rules. In: LIN, Tsau Young et.al.(eds.). Foundations of Data Mining and Knowledge Discovery. Berlin : Springer, 2005, s. 211-231.
S. Džeroski, N. Lavrač. Relational data mining, Springer 2001
T. Horváth, P. Vojtáš, Induction of Fuzzy and Annotated Logic Programs, in Revised Selected Papers from ILP 2006, S. Muggleton, R. Otero, and A. Tamaddoni-Nezhad (Eds.), LNAI 4455, pp. 260-274, 2007 Last update: T_KSI (28.04.2008)
|
|
||
|
Motivation examples, data, relations to formal models Academic software systems LISp-Miner, Ferda and WEKA and their applications GUHA method - principle, important GUHA procedures, problems of implementation Relation of GUHA method to classical methods (association rules. Apriori algorithm, decision tress) Multi-relational data mining Inductive logic programming Ordinal classification Data mining and Semantic Web Automatic reporting data mining results Observational calculi and their application in data mining Application of knowledge engineering methods in data mining Overview of current trends Last update: T_KSI (28.04.2008)
|