Title :
Mining fuzzy association rules in incomplete databases
Author :
Arotaritei, Dragos
Author_Institution :
Dept. of Comput. Sci. & Eng., Aalborg Univ., Denmark
fDate :
6/24/1905 12:00:00 AM
Abstract :
Mining quantitative association rules is a particular subject of interest in fuzzy set application theory. However, the theory generally applies to a transactional database with no missing values. A predictive algorithm is proposed in this paper in order to extrapolate (interpolate) the unknown values. A fuzzy data mining algorithm is used to discover fuzzy association rules over the extended database with filled predictive values
Keywords :
data mining; extrapolation; fuzzy set theory; interpolation; very large databases; data mining; extrapolation; fuzzy association rules; fuzzy set theory; incomplete databases; interpolation; predictive algorithm; Application software; Association rules; Computer science; Data mining; Decision trees; Filling; Fuzzy set theory; Multivalued logic; Relational databases; Transaction 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.2002.1004998
