【摘要】 提出了一种Apriori算法的改进算法 ,讨论了通过使用概率的方法估算任意数据项集同时出现的概率来求候选频繁项集的方法 ,并给出了相关算法。将本算法与Apriori算法产生的候选项集大小和扫描数据库次数进行了比较 ,该算法将关联规则挖掘的运行速度提高了一个数量级 ,非常适合挖掘数据库、长模式的关联规则。更多还原

【Abstract】 Based on Apriori algorithm,an improved algorithm of Apriori is presented in this paper.The main ideas of the algorithm are: 1) Count the probability of each item( I 1,I 2,...,I m ) by scanning the DB for the first time; 2) Item I k and I m appear in one record is P km . min( P k,P m)≤P km ≤P k*P m , 3) Count the support count of the frequence itemsets by scanning the DB another time; 4) Output the association rule from the frequence itemsets. The detailed algorithm and its sample are described in this paper.Lastly we compare it with Apriori algorithm.The best quality of the new algorithm is that it reduce the times of scanning DB.更多还原