A Stream Sequential Pattern Mining Model

摘要：

Stream is continuous, fast, dynamic and unlimited. Data stream cannot be stored in second storages for multiple scanning. In this paper, we propose a multiple level sequential pattern mining model, which is adapted to stream characteristic. It implements traditional mining algorithm over in-memory data to acquire accurate sequential patterns from data of ranges of stream. Besides, the model splits the memory into many levels to store sequential patterns under different minimum supports. In addition, this paper discusses the construction process of parameter optimization. Finally, a series of experiments is implemented to prove the effectiveness and efficiency of this model.

关键词： Sequential Pattern Data Stream

作者: Haifeng Li

作者单位: School of Information, Central University of Finance and Economics Beijing, China

会议类型: 国际会议

会议名称: 2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

会议地点: 哈尔滨

会议语种:英文

页码: 704-707

在线出版日期: 2011-12-24（万方平台首次上网日期，不代表论文的发表时间）