Mining Approximative Descriptions of Sets Using Rough Sets

D. Simovici, Selim Mimaroglu
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Abstract

Using concepts from rough set theory we investigate the existence of approximative descriptions of collections of objects that can be extracted from in data set, a problem of interest for biologists that need to find succinct descriptions of families of taxonomic units.  Our algorithm is based on an anti-monotonicity of borders of object set and makes use of an approach that is, ina certain sense, a dual of the Apriori algorithm used inidentifying frequent item sets.
利用粗糙集挖掘集的近似描述
利用粗糙集理论的概念,我们研究了可以从数据集中提取的对象集合的近似描述的存在性,这是生物学家需要找到分类单位科的简洁描述的一个感兴趣的问题。我们的算法基于对象集边界的反单调性,并使用了一种方法,在某种意义上,是用于识别频繁项集的Apriori算法的对偶。
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