一种基于概念格的关联规则挖掘算法

Algorithm for mining association rules based concept lattice

关联规则挖掘是数据挖掘中的一项核心任务,而由二元关系导出的概念格则是一种非常有用的形式化分析工具,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。一个概念内涵与一个关联规则中的闭合项集可以一一对应。提出了一种新有基于概念格的关联规则挖掘算法Arca(Association Rule based Concept lAttice)。Arca算法通过概念矩阵构造部分概念格,使概念格中的每个概念对应一个闭合频繁项集。然后生成一些关联规则,在这些关联规则上通过定义了四个算子来生成了所有关联规则。

Association rule discovery is one of kernel tasks of data mining.Concept lattice,induced from a binary relation between objects and features,is a very useful formal analysis tool.It represents the unification of concept intension and extension.It reflects the association between objects and features,and the relationship of generalization and specialization among concepts.There is a one-to-one correspondence between concept intensions and closed frequent itemsets.This paper presents an efficient algorithm for mining association rules based concept lattice called Arca （Association Rule based Concept lAttice）.Arca algorithm uses conceptmatrix to build a part of concept lattice,in which the intension of every concept be put into one-to-one correspondence with a closed frequent itemset.Then all association rules are discovered by 4 operators which are defined in this paper performed on these concepts