Relative association rules based on rough set theory

Authors:
Shu-Hsien Liao;Yin-Ju Chen;Shiu-Hwei Ho
Affiliations:
Department of Management Sciences, Tamkang University, New Taipei City, Taiwan R.O.C;Graduate Institute of Management Sciences, Tamkang University, New Taipei City, Taiwan R.O.C;Department of Business Administration, Technology and Science Institute of Northern Taiwan, Taipei, Taiwan, R.O.C
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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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.