Rule-preserved object compression in formal decision contexts using concept lattices

Rule-preserved object compression in formal decision contexts using concept lattices
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使用概念格在正式决策上下文中保留规则的对象压缩

DOI:
10.1016/j.knosys.2014.08.020
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发表时间:
2014-11
影响因子:
8.8
通讯作者:
Xiao Zhang
Xiao Zhang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Jinhai Li;Changlin Mei;Junhong Wang;Xiao Zhang

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规则获取是形式化决策上下文分析的主要目的之一。一般来说,给定一个正式的决策上下文,它的一些对象可能不是规则获取所必需的。本研究探讨减少正式决策上下文的对象集,而不丢失整个对象集所提供的决策规则信息的问题。使用概念格,我们提出了一个理论框架的对象压缩的正式决策上下文。在此框架下,证明了从约简数据库中得到的所有非冗余决策规则集相对于初始形式化决策上下文是完备的。此外,一个完整的算法来计算一个正式的决策上下文的约简。对实际数据库的分析表明,该方法可以大大减小形式化决策上下文的规模,并且与澄清上下文和行缩减上下文技术相比,可以删除更多的对象。
Rule acquisition is one of the main purposes in the analysis of formal decision contexts. In general, given a formal decision context, some of its objects may not be essential to the rule acquisition. This study investigates the issue of reducing the object set of a formal decision context without losing the decision rule information provided by the entire set of objects. Using concept lattices, we propose a theoretical framework of object compression for formal decision contexts. And under this framework, it is proved that the set of all the non-redundant decision rules obtained from the reduced database is sound and complete with respect to the initial formal decision context. Furthermore, a complete algorithm is developed to compute a reduct of a formal decision context. The analysis of some real-life databases demonstrates that the proposed object compression method can largely reduce the size of a formal decision context and it can remove much more objects than both the techniques of clarified context and row reduced context.
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