基于多变量提升度量在Apriori算法中的研究与应用

Mining of association rules is an important research topic among the various data mining problems. However the common approaches based on support-confidence frame work maybe get a great number of redundant and wrong association rules. In order to solve the problems, an upgrade measure is defined and added to the mining algorithm for association rules. The lifting measure of the binary variables is extended to that of multiple variables，such that it is more suitable for large databases. The experimental result shows that introducing lift measure based on common approach to association rules mining can reduce the useless association rules, and mine a lot of interesting association rules. It is a more ideal model mining association rules.