摘要： 在会话流中挖掘Top-k闭序列模式，存在因相关比率ρ的大小而导致的内存消耗和挖掘精度之间的冲突。基于False-Negative方法，提出Tstream算法，制定2种约束策略限制ρ。基于该策略设计加权调和计数函数，渐进计算每个模式的支持度。实验结果证明了该算法的有效性。

关键词: Top-k闭序列模式, 加权调和平均数, 调节因子

Abstract: The current methods in session streams for mining Top-k Closed Sequential Pattern(Topk_CSP) may lead to a conflict between output precision and memory consumption because of using ρ. This paper proposes TStream algorithm, which is based on False-Negative approach. TStream utilizes two constraint strategies to restrict ρ, and employs a weighted harmonic count function to calculate the support of each pattern progressively. Experimental results show that the algorithm is efficient.

Key words: Top-k Closed Sequential Pattern(Topk_CSP), Weighted Harmonic Average(WHA), regulatory factor

中图分类号: 

• TP311