AGRAWAL R, IMIELINSKI T, SWAMI A. Mining association rules between sets of items in large database[A].Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data[C]. Washington: ACM,1993. 207-216.
[2]
ZHONG N, YAO Y, OHSUGA S. Peculiarity oriented multi-database mining[A]. In: ZYTKOW J, RAUCH J.Principles of Data Mining and Knowledge Discovery[M]. Berlin: Springer-Verlag, 1999. 136-146.
[3]
NIECHUYS S, WOLF R Foundations of Inductive Logic Programming[M]. Berlin: Springer, 1997. 163-177.
[4]
FAYYAD U Knowledge discovery in databases: An overview[A]. In: DZEROSKI S, LAVRAC N. RelationalData Mining[C]. Berlin: Springer, 2001. 29-47.
[5]
QUINLAN J. Learning logical definitions from relalions[J]. Machine Learning, 1990, 5: 239-266.
[6]
ZHONG N, YAO Y, OHSHIMA M, et al. Interestingness, peculiarity, and multi-database mining[A]. Proc 2001IEEE International Conference on Data Mining (IEEE ICDM'0l)[C]. Washington: IEEE Computer Society, 2001. 566-573.
[7]
LIU H, SETIONO R Feature selection via discretization of numeric attributes[J]. IEEE Trans Knowledge and DataEng, 1997, 9(4) : 642-645.
[8]
LIN T. Granular computing on binary relations 1: Data mining and neighborhood systems[A]. In: POLKOWSKI L,SKOWRON A. Rough Sets in Knowledge Discovery 1, in Studies in Fuzziness and Soft Computing Series[C].Heidelberg: Physica-Verlag, 1998. 107-121.