NDLI: Relational peculiarity oriented data mining

Content Provider	IEEE Xplore Digital Library
Author	Ning Zhong Chunnian Liu Yao, Y.Y. Ohshima, M. Mingxin Huang Jiajin Huang
Copyright Year	2004
Description	Author affiliation: Dept. of Inf. Eng., Maebashi Inst. of Technol., Japan (Ning Zhong)
Abstract	Peculiarity rules are a new type of interesting rules which can be discovered by searching the relevance among peculiar data. A main task of mining peculiarity rules is the identification of peculiarity. Traditional methods of finding peculiar data are attribute-based approaches. This paper extends peculiarity oriented mining to relational peculiarity oriented mining. Peculiar data are identified on record level, and peculiar rules are mined and explained in a relational mining framework. The results from preliminary experiments show that relational peculiarity oriented mining is very effective.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Computational Intelligence (TCCI) IEEE Comput. Soc. Tech. Comm. on Pattern Analysis and Machine Intelligence (TCPAMI)
Starting Page	575
Ending Page	578
File Size	144130
Page Count	4
File Format	PDF
ISBN	0769521428
DOI	10.1109/ICDM.2004.10008
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Relational databases Computer science Logic programming Learning systems Data engineering Educational institutions Laboratories Brain modeling Image databases
Content Type	Text
Resource Type	Article

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