A relative association rules based on rough set theory

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Title:	A relative association rules based on rough set theory
Authors:	Liao, Shu-hsien;Chung, Y.J.;Ho, S.H.
Contributors:	淡江大學管理科學學系
Keywords:	Rough set;Data mining;Relative association rule;Ordinal data
Date:	2011-11
Issue Date:	2013-08-12 16:26:47 (UTC+8)
Abstract:	The traditional association rule that should be fixed in order to avoid the following: only trivial rules are retained and interesting rules are not discarded. In fact, the situations that use the relative comparison to express are more complete than those that use the absolute comparison. Through relative comparison, we proposes a new approach for mining association rule, which has the ability to handle uncertainty in the classing process, so that we can reduce information loss and enhance the result of data mining. In this paper, the new approach can be applied for finding association rules, which have the ability to handle uncertainty in the classing process, is suitable for interval data types, and help the decision to try to find the relative association rules within the ranking data.
Relation:	18th International Conference on Neural Information Processing 2011, (ICONIP2011)
DOI:	10.1007/978-3-642-24958-7_22
Appears in Collections:	[Department of Management Sciences] Proceeding

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