介粒度空间中的最优粒度选择和属性约简

Optimal Granularity Selection and Attribute Reduction in Meso-granularity Space

以往的形式概念分析采用介粒度形式背景,满足对数据跨层粒化的需求,但其既没有将寻找最优粒度和属性约简有效结合起来,又没有在多粒度的背景下高效地解决组合爆炸问题。为此,基于介粒度中粒度选择和属性约简的联系,提出了一种新的最优粒度选择方式——最优粒度约简,以同步进行粒度选择和属性约简。鉴于寻找最优粒度约简存在组合爆炸的问题,设计了逐步搜索方法,通过已搜索的信息更新粒度空间,去除大量非最优粒度约简,显著提高了搜索效率。实验结果表明了所提方法的有效性和优势。

The conventional formal concept analysis adopts a meso-granularity formal context to meet the requirements of cross-layer granulation of data.However,it does not effectively combine the search for optimal granularity with attribute reduction,nor does it efficiently solve the problem of combination explosion in a multi-granular context.Therefore,based on the connection between granularity selection and attribute reduction in the meso-granularity,a new optimal granularity selection method(i.e.,optimal granularity reduction)is proposed to synchronize the selection of the optimal granularity and attribute reduction.In view of the combination explosion in searching for optimal granularity reduction,a stepwise search method is designed to update the gra-nularity space with searched information,eliminating a large number of non-optimal granularity reduction and significantly improving search efficiency.Experimental results demonstrate the effectiveness and superiority of this method.