Title :
Semantics oriented association rules
Author :
Louie, Eric ; Lin, T.Y.
Author_Institution :
IBM, Almaden Res. Center, San Jose, CA, USA
fDate :
6/24/1905 12:00:00 AM
Abstract :
It is well known that relational theory carries very little semantic. To mine deeper semantics, additional modeling is necessary. In fact, some "pure" association rules are found to exist even in randomly generated data. We consider a relational database in which every attribute value has some additional information, such as price, fuzzy degree, neighborhood, or security compartment and levels. Two types of additions are considered: one is structure added, the other is valued-added. Somewhat a surprise, the additional cost in semantics checking is found to be very well compensated by the pruning of non-semantic rules
Keywords :
data mining; data models; fuzzy set theory; relational databases; attribute value; fuzzy degree; neighborhood; nonsemantic rules pruning; price; pure association rules; randomly generated data; relational database; security compartment; semantics checking; semantics oriented association rules; structure added data models; valued added data model; Association rules; Costs; Data mining; Data models; Data security; Frequency; Fuzzy systems; Information security; Mathematical model; Relational databases;
Conference_Titel :
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7280-8
DOI :
10.1109/FUZZ.2002.1006633
