DocumentCode
2331640
Title
Case base maintenance based on outlier data mining
Author
Ni, Zhi-wei ; Liu, Yu ; Li, Feng-gang ; Yang, Shan-Lin
Author_Institution
Inst. of Comput. Network Syst., Hefei Univ. of Technol., China
Volume
5
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
2861
Abstract
In case-based reasoning (CBR) system, as the scale of case base is enlarging, the system performance is gradually dropping. This paper mainly discusses how to maintain case bases in CBR system by adopting outlier data mining and case sieving techniques. Experimental results have shown that the proposed algorithm can maintain case bases satisfactorily and stably, thus assuring the good performance of CBR system.
Keywords
case-based reasoning; data mining; case base maintenance; case sieving; case-based reasoning; outlier data mining; Computer aided software engineering; Computer networks; Data mining; Electronic mail; History; Humans; Information filtering; Large-scale systems; Problem-solving; System performance; Case base; Case base maintenance; Case-based reasoning; Outlier mining; data mining;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527430
Filename
1527430
Link To Document