概念格与关联规则发现

作为数据挖掘核心任务之一的关联规则发现已经得到了广泛的研究。而由二元关系导出的概念格则是一种非常有用的形式化工具。它体现了概念内涵和外延的统一，反映了对象和特征间的联系以及概念间的泛化与例化关系，因此非常适于发现数据中潜在的概念。分析了概念格与关联规则提取之间的关系，根据需要对格结构进行了相应的修改，提出了相应的渐进式生成算法和基于概念格的关联规则提取算法，通过定理和性质对算法进行了说明并对关联规则进行缩减。最后对格结构的复杂性进行了讨论并给出了相应的实验结果。

CONCEPT LATTICE AND ASSOCIATION RULE DISCOVERY

Association rule discovery, as a kernel task of data mining, has been studied widely. Concept lattice, induced from a binary relation between objects and features, is a very useful formal tool and has been used in many fields. It realizes the unification of concept intension and concept extension, represents the association between objects and features, and reflects the relationship of generalization and the specialization among concepts, so it is fit for discovering the potential concept below the data. In this paper, the relationship between concept lattice and association rule discovery is analyzed. Then, the structure of node in lattice is modified according to the requirement, while two algorithms are developed for constructing the corresponding lattice incrementally and for extracting association rules, where some theorems and properties are used to reduce the number of discovered rules. Finally, the complexity problem is discussed, and the corresponding experimental results are given.