中文摘要：

形式概念分析是知识表示与处理的一种实用数学方法，因其核心工具概念格的构造代价涉及指数时间复杂度，它在一定程度上导致其处理数据效率不高，这个问题也一直阻碍着该理论的快速发展与广泛应用。粒计算以粒的形成、粒的转移、粒的合成与分解等手段有效解决问题而著称，它允许问题在各个粒化层面上得到处理，并根据实际需要在解决问题的精度与耗时之间做出权衡。形式概念分析的粒计算方法的主要研究目标是将粒计算的这些优势融入传统形式概念分析中以有效解决数据分析与处理问题。具体地，本文从Galois连接的粒计算模型、对象粒化、属性粒化、关系粒化、关系诱导的概念粒化、粒规则、粒约简、粒概念、粒概念学习、概念粒计算系统等角度展示形式概念分析的粒计算方法的主要研究内容，并针对大数据与认知学习提出若干挑战性问题。有关讨论结果将为形式概念分析的粒计算方法的研究与发展提供借鉴。

英文摘要：

Formal concept analysis is a useful mathematical method for knowledge representation and processing and its key tool is concept lattice. However, the construction of concept lattice takes exponential time complexity, which to some extent makes data processing inefficient and hinders fast development of this theory and its application. Granular computing is well-known for formation of granule, transformation of granule, and synthesis and decomposition of granule. Granular computing allows to consider problem by granularity in various levels, and strikes a balance between accuracy and time consuming in solving problem based on the practical requirements. The main research aim of granular computing approach for formal concept analysis is to incorporate these advantages of granular computing into traditional formal concept analysis for efficiently solving data analysis and processing. More specifically, this paper shows the main research topics of granular computing approach for formal concept analysis from the perspectives of Galois connection based granular computing model, object granule, attribute granule, relation granule, relation-based concept granularity, granular rule, granular reduct, granular concept and learning, and concept granular computing systems. In addition, some challenging problems are also proposed for dealing with big data and cognitive learning. The obtained results will provide some references for the further study of granular computing approach of formal concept analysis.