Title :
Mining fuzzy quantitative association rules
Author_Institution :
Div. of Comput. Sci., Texas Univ., San Antonio, TX, USA
Abstract :
Given a relational database and a set of fuzzy terms defined for some attributes we consider the problem of mining fuzzy quantitative association rules that may contain crisp values, intervals, and fuzzy terms in both antecedent and consequent. We present an algorithm extended from the equi-depth partition (EDP) algorithm for solving this problem. Our approach combines interval partition with pre-defined fuzzy terms and is more general
Keywords :
data mining; database theory; fuzzy logic; relational databases; crisp values; equi-depth partition algorithm; fuzzy quantitative association rule mining; fuzzy terms; interval partition; relational database; Association rules; Clustering algorithms; Computer science; Data mining; Fuzzy sets; Marine vehicles; Marketing management; Read only memory; Relational databases; Transaction databases;
Conference_Titel :
Tools with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7695-0456-6
DOI :
10.1109/TAI.1999.809772
