Association Rule Learning Algorithm Based On Extension

With data mining technology getting mature,it plays a very important role indecision support activities in various disciplines. One of its important tasks is to find theassociation rules in the database namely to identify the valuable rules or associationamong sets of items in data. Introduction to data mining extension has opened up newmining algorithm for data mining, provided new ideas and methods for data mining.This paper based on the general data mining technology and making full use ofextension knowledge combining the Extension Element Theory and data miningalgorithms, builds a database of meta-models.Apriori algorithm in relational databasesis made on the basis of the extension data mining algorithm of association rules.Thisalgorithm converts Boolean values into multi-valued attributes in relational databasesand databases become intuitive and simple, therefore that makes the expression moreclear and reduces the amount of data in the next rule mining.A specific experiment isprovided to validate the feasibility of this algorithm.Finally,the paper summarizes themain idea and gives further research direction.