多粒度形式背景的不确定性度量与最优粒度选择

多粒度形式概念分析是数据挖掘与知识发现的重要工具,但现有的多粒度形式概念分析理论中并未提出选择最优形式背景的标准,这导致只能对多个单粒度形式背景逐一研究其知识发现问题,因此无法应对含有多个粒度属性的形式背景.鉴于此,对多粒度形式背景的粒度树上的属性块进行组合,将信息熵作为组合形式背景优劣的判别标准以评价最优粒度选择的性...

Uncertainty measurement and optimal granularity selection for multi-granularity formal context

Multi-granularity formal concept analysis is an important tool for data mining and knowledge discovery. However, there is no standard to select an optimal formal context in the existing multi-granularity formal concept analysis theory, which leads to the fact that multiple single-granularity formal contexts have to be studied separately one by one for achieving the task of knowledge discovery, leaving the formal contexts with multi-granularity attributes unexplored. In this paper, how to combine attribute blocks of the granularity tree of a multi-granularity formal context is studied, and information entropy is used as a criterion to judge whether a combined formal context is good or not, so as to evaluate the performance of the obtained optimal granularity selection results. Firstly, based on a granularity tree, the notion of a generalized meso-granularity pruning formal context is proposed, which can realize not only inter-layer cross-granularity combination but also cross-layer combination of attribute blocks. Secondly, the information entropy of a generalized meso-granularity pruning formal context is defined to evaluate its advantages and disadvantages, and an optimal granularity selection algorithm is designed. Then, the information entropy is used to measure the importance of the multi-granularity pruning class-attribute block and granularity tree. Finally, experimental analysis shows the effectiveness of the proposed methods of optimal granularity selection and importance measurement of a granularity tree based on information entropy.