关联规则挖掘算法的改进与优化研究

Research of Improvement and Optimization on Association Rules Mining Algorithm

首先对Apriori算法过程本身进行了详细的研究,给出了三种改进措施,各种改进措施在特定的应用场合有着明显的优点,均能有效减少存储候选集所占用的空间或算法过程占用的时间,之后着重对强关联规则的生成算法进行了详细讨论,给出了优化算法,实例表明该算法能有效减少相关计算量,比已有算法运算效率明显提高.

The most classical algorithm Apriori was studied and then separated into six steps:linking→cutting→generating C_k→scanning and counting→comparing→generating L_k. After that three improved measures were provided:1) judge if c∈C_k,2) scan while selecting,3) scan directly while bypassing cutting.Every measure has obvious advantages under special situation,and can effectively reduce the space occupied by candidate itemset and the time occupied by steps.Then a deep research was made on association rules generating process in detail,and a developed algorithm was given.The developed algorithm can decrease related computation quantity in large scale and improve the efficiency of the algorithm.