约束概念格及其构造方法

概念格是一种有效的数据分析和知识提取的形式化工具．然而，随着要处理的数据量的剧增，基于原始形式背景构造出的概念格结点数目庞大，占用大的存储空间，同时概念格结点中一些属性集形成的内涵，用户并不都感兴趣，因而从中提取用户需求知识费时．为了降低概念格构造的时空复杂性，增强实用性和针对性，首先采用谓词逻辑描述用户感兴趣的背景知识，并将背景知识引入到概念格结构中，提出了一种新的概念格：约束概念格．在此基础上，提出了基于背景知识的约束概念格构造算法CCLA．理论分析表明，该算法能有效地减少概念格的存储空间和建格时间．最后，采用恒星天体光谱数据作为形式背景，实验验证了该算法的有效性．

Constrained concept lattice and its construction method

Concept lattice is an effective formal tool for data analysis and knowledge mining.However,with the increase of data volume,the node number of the constructed concept lattice from the original formal context usually increases enormously,and large storage is required accordingly.Meantime,users are not interested in all intensions of attributes set,and more computational time is unnecessarily consumed as a result.In order to reduce time and storage complexity and improve the utility and pertinence to the concept lattice construction,predicate logic is used to describe the user interested background knowledge,and a new concept lattice structure-constrained concept lattice is presented.Then based on the background knowledge,a construction algorithm(CCLA)is also provided.Through some theoretical analysis,it is shown that the proposed algorithm can reduce the storage and time complexity of concept lattice construction process.Finally,the experiments with celestial body spectra as the formal context validate the proposed algorithm.