概念格在不完备形式背景中的知识获取模型

为了使概念格模型具有更强的数据处理能力，消除不完备信息带来的影响，针对经典概念格的局限性，本文将粗糙集中的粒化思维融入到概念格中。首先探讨了概念格视角下的信息粒化方法，然后提出了基于等价类和基于极大相容类的知识获取方法，最后给出了实例分析。这些方法一方面有助于概念格与粗糙集的融合，另一方面也为探索不完备形式背景的分析处理机制提供了有益思路。

Knowledge acquisition model of concept lattice in an incomplete formal context

To improve the data processing ability of the concept lattice model and eliminate the impact of incomplete information, in this paper, considering the limitations of classical concept lattice in actual applications, the granulation idea in rough sets is integrated into concept lattice. First, we explore information granulation methods from the perspective of concept lattice and then propose a knowledge acquisition method based on the equivalence class and maximal tolerance class, and then, carry out the case analysis. These methods are helpful for the integration of concept lattice and rough sets. Moreover, they also provide some useful ideas for exploring the analysis and processing mechanism of incomplete formal contexts.