The research of association rules mining algorithm based on binary

An algorithm of association rules mining based on binary has been introduced to solve two problems that how to easily generate candidate frequent itemsets and fast compute support. However the basic notion of presented algorithms in generating candidate itemsets is still similar to Apriori. In some degree the efficiency of these algorithms is very confined, and so this paper proposes two different searching strategies of association rules mining algorithms based on binary, which are suitable for mining corresponding characteristic database. One is that the notion of generating candidate frequent itemsets is similar to up-down searching of traditional association rules mining algorithm, which uses the method of forming subset to generate candidate frequent itemsets from long to short and is suitable for mining long frequent itemsets. The other is that the method of increasing value is used to generate candidate frequent itemsets, which is more efficient than Apriori based on binary and is more suitable for mining short frequent itemsets, in this mining course length of candidate frequent itemsets crossways varies from short to long. The both algorithms use digital character to reduce the number of scanned transactions. The experiment based on above three algorithms indicates that the efficiency of two presented algorithms is fast and efficient when mining corresponding characteristic database.