面向对象多粒度概念格的构造

如何降低概念格构造过程中的复杂度是形式概念分析中一个重要的研究课题．粒计算可以从多个角度对问题进行分析、求解，已经成为人工智能领域中研究问题的有效工具．针对多粒度概念格的快速构造问题，基于粒计算理论，本文研究了属性粒度变化前后概念外延与内涵之间的关系，定义了不同类型的概念，提出了基于不同属性粒度组合的面向对象概念格转换算法---Zoom，具体包括对属性粒度进行细化的Zoom-in算法和对属性粒度进行粗化的Zoom-out算法．Zoom算法在原概念格的基础上直接实现新概念格的构造，避免了遵循传统的概念格构造方法时从新形式背景中生成新面向对象概念格的繁琐步骤，从而提高了概念格的构造效率，可以快速确定概念格的属性最优粒度组合，进而有助于在数据中进行数据挖掘和知识发现．

The Construction of Object-oriented Multi-granularity Concept Lattice

How to reduce the complexity in the construction of concept lattice is an important research topic in formal concept analysis. Granular computing can analyze and solve problems from multiple angles which has been an effective tool for solving problems in the field of artificial intelligence. Aiming at the rapid construction of multi-granularity concept lattice, based on the granular computing theory, this paper investigates the relation between the extent and intent of concept before and after the change of the attribute granularity and defines different types of concepts. Then, Zoom algorithms composed of Zoom-in algorithm and Zoom-out algorithm are proposed, which achieve the transformation among object-oriented concept lattices with different attribute granularity combinations. Based on the original concept lattice, Zoom algorithms implement the construction of the new concept lattice directly, which avoid the trivial steps of the traditional construction method of concept lattice, and improve the efficiency of the construction of concept lattice. Therefore, the optimal granularity combination of the concept lattice can be quickly determined, which ultimately helps data mining and knowledge discovery from the data.