提出了一种新的多维序列模式挖掘算法,首先在序列信息中挖掘序列模式,然后针对每个序列模式,在包含此模式的所有元组中的多维信息中挖掘频繁1-项集,由得到的频繁1-项集开始,循环的由频繁(k-1)-项集(k〉1)连接生成频繁k项集,从而得到所有的多维模式。该算法通过扫描不断缩小的频繁(k-1)-项集来生成频繁k项集,减少了扫描投影数据库的次数,因而减少了时间开销,实验表明该算法有较高的挖掘效率。
This paper proposes a new algorithm for mining multi-dimensional sequential patterns.The algorithm mines sequential patterns in dataset firstly,and then finds frequent 1-itemset from multi-dimensional information that support this pattern in dataset for every sequence pattern,and generates frequent k-itemset from frequent (k-1)-itemset.This algorithm gets the frequent k-itemset through scanning the frequent (k-1)-itemset that reduce gradually,so it saves time.Experiment shows this method has good efficiency.