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.