The Research Of Judgment Criterion Of Association Rules And It's Algorithms

In recent years, knowledge discovery in database which also be called data mining, receives the artificial intelligence and widespread value in database field. Mining association rules is an important topic of data mining research. Its main research object is the transaction database, its essential target is to discover whether there is certain connection relation between items in transaction database. Researchers domestic and foreign all to carries on positively thoroughly studies of the association rules mining, and proposes very many algorithms.So that we offered a new formation of the definition of association rules.Presently the formalize definition of association rules is adopted the formation of X ?Y, there into X as to precondition,Y as to subsequence. But actually we only know whether Items be sold together.We don't know whether X induce Y or Y induce X.So the formation of X?Y is not fit to the fact,it may lead to wrong results.The present judgment criteria of association rules are a support and a confidence .if the association rules are generated according to the criteria, a lot of them are invalid and false. To reduce invalid rules in mining association rules, we have analyzed the reasons and presented to add the effect or the relative confidence, we classify strong association rules into positive, invalid and negative association rules. We offer improving algorithm of mining association rules with new judgment criterion.We carries on careful analysis to classics algorithm like Apriori, FP Growth algorithm, and offered their improvement forms.Studies the existing judgment criteria of data mining, makes the improvement in view of its lacks, and propose a new formation of the definition of association rules.