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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/91972

    Title: Relative Association Rules Based on Rough Set Theory
    Authors: Liao, Shu-hsien;Chen, Yin-ju;Ho, Shiu-Hwei
    Contributors: 淡江大學管理科學學系
    Keywords: Rough set;Data mining;Relative association rule;Ordinal data
    Date: 2011-06-01
    Issue Date: 2013-08-12 13:09:53 (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: Lecture notes in Computer Science 7063, pp.185-192
    DOI: 10.1007/978-3-642-24958-7_22
    Appears in Collections:[Department of Management Sciences] Journal Article

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