Relational Peculiarity Oriented Data Mining

I
ICDM
2004
Fourth IEEE International Conference on Data Mining (ICDM'04)
Abstract - Relational Peculiarity Oriented Data Mining

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Fourth IEEE International Conference on Data Mining (ICDM'04)

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	Ning Zhong, Chunnian Liu, Y. Y. Yao, Muneaki Ohshima, Mingxin Huang, Jiajin Huang, "Relational Peculiarity Oriented Data Mining," Data Mining, IEEE International Conference on, pp. 575-578, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10008, author = {Ning Zhong and Chunnian Liu and Y. Y. Yao and Muneaki Ohshima and Mingxin Huang and Jiajin Huang}, title = {Relational Peculiarity Oriented Data Mining}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {575-578}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10008}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Relational Peculiarity Oriented Data Mining SN - 0-7695-2142-8 SP575 EP578 A1 - Ning Zhong, A1 - Chunnian Liu, A1 - Y. Y. Yao, A1 - Muneaki Ohshima, A1 - Mingxin Huang, A1 - Jiajin Huang, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Ning Zhong, Maebashi Institute of Technology, Japan

Chunnian Liu, Beijing University of Technology, China

Y. Y. Yao, University of Regina, Canada

Muneaki Ohshima, Maebashi Institute of Technology, Japan

Mingxin Huang, Beijing University of Technology, China

Jiajin Huang, Beijing University of Technology, China

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10008

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.

Citation:

Ning Zhong, Chunnian Liu, Y. Y. Yao, Muneaki Ohshima, Mingxin Huang, Jiajin Huang, "Relational Peculiarity Oriented Data Mining," icdm, pp.575-578, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

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