基于概念格的规则产生集挖掘算法

传统的规则提取算法产生的规则集合相当庞大，其中包含许多冗余的规则．使用闭项集可以减少规则的数目，而概念格结点问的泛化和例化关系非常适用于规则提取．基于概念格理论和闭项集的概念，提出了一种新的更有利于规则提取的格结构，给出了相应的基于闭标记的渐进式构造算法和规则提取算法．最后提供给用户的是直观的、易理解的规则子集，用户可以有选择地从中推导出其他的规则．实验表明该方法能够高效地挖掘规则产生集．

An Algorithm for Extracting Rule-Generating Sets Based on Concept Lattice

The rule sets extracted by traditional algorithm are usually very large, because it includes many redundant rules. The number of rules can be reduced using closed item sets. The relationship of generalization and specialization among concepts of concept lattice is very suitable for extracting rules. A new and more advantageous lattice structure for extracting rules is proposed based on the theory of concept lattice and the concept of closed item set. Then, an incremental algorithm based on closed label for constructing lattice and algorithm for rules extracting are developed. Finally, a visual and easily understandable set of rules is presented to user, who can selectively derive other rules of interest. The example shows that the algorithm used in this paper can efficiently extract rule-generating set.