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A New Attribute Reduction Recursive Algorithm Based On Granular Computing

机译：基于粒度计算的新属性缩减递归算法

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—Existing representative research achievement of attribute reduction mainly focused on two aspects. One is how to improve the efficiency of attribute reduction algorithms for all attributes including the added properties. Such as the recursive algorithm to change conjunctive normal form into disjunctive normal form based on the Boolean matrix and algorithm based on radix sorting for computing core and reductions of a given information system, etc. On the other hand focus on objects recursive algorithms. The drawback is that these methods have not fully use knowledge gained when some attributes was added to a discussion on domain. Therefore, in this paper, the regularity of core and reduction’s changes under adding new attributes into a given information system were discussed. Moreover, the new incremental recursive reduction algorithms from an information system were proposed based on Granular computing. Experiments show that these algorithms can quickly and exactly calculate new core and reduction of new information system by taking advantage of knowledge of previous information system.
机译：- 提出代表性的研究成果的属性减少主要集中在两个方面。一个是如何提高所有属性的属性缩减算法的效率，包括添加属性。如递归算法基于基于Radix分类的布尔矩阵和算法将联合正常形式改变为分离正常形式，用于计算核心和给定信息系统的计算核心等。另一方面，对象递归算法。缺点是，当向域的讨论中添加某些属性时，这些方法没有完全使用知识。因此，在本文中，讨论了将新属性添加到给定信息系统中的核心和减少变化的规律性。此外，基于粒度计算提出了来自信息系统的新增量递归算法。实验表明，通过利用先前信息系统的知识，这些算法可以快速，准确地计算新的核心和对新信息系统的减少。

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