关联规则的高效向量法数据挖掘

An Efficient Vector-based Algorithm for Data Mining Association Rules

对经典的向量挖掘算法和Apriori算法的思想及其复杂度进行分析后 ,提出了一种新的高效向量数据挖掘算法。新算法通过避免不必要的计算以达到提高算法的计算效率 ,通过避免不必要的存贮以达到减少算法的空间复杂度 ,与经典的向量挖掘算法相比有如下优点 :(1)空间复杂度为o(n|L1|) ,比经典的挖掘算法的空间复杂度要小得多 ;(2 )计算量比经典的挖掘算法要小。

After the thought and complexity of classical vector data mining algorithm and Apriori algorithm are analyzed,a new efficient vector data mining algorithm has been put forward.The computation efficiency of the algorithm is improved through avoiding the unnecessary computation;on the other hand,the space complexity of the algorithm is decreased through reducing the unnecessary storing.Compared with the classical vector data mining algorithm, this new algorithm has mainly two advantages,one is that the space complexity is o(n｜L_1｜),which is much lower than that of the classical vector data mining algorithm;the other is that the computing rate is declined too a large extent.