Considering the problems aroused by the traditional association rules visualization mining methods which are lack of dealing with multi-valued attribute data

especially not conducive to expressing the frequent pattern between items and representing multi-schema association rules

this paper

which presents the redefinition and classification of multi-valued attribute data by using conceptual lattice

proposes an improvement of Apriori algorithm based on the KAF factor and the CHF factor to mine multi-valued attribute association rules as well as introduces a novel visualizing approach for multi-valued association rules based on concept lattice

and establishes a complete mining course parameters adjustment strategy acting very well in improving the speed and efficiency of mining algorithm

which is convenient for users to select key attribute values to mine and analyze rules.This methodology organically organizes the multi-valued attribute data with concept lattice structure

which has achieved frequent itemset visualization and multi-schema visualization of association rules.The experimental results turn out that the improved mining algorithm has a better performance and the schema has much excellent visual effects for multi-schema association rules visualization.