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基于序列划分的压缩序列模式挖掘算法

Mining compressed sequential patterns by ranging the closed sequential patterns
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摘要 研究了静态数据库当中挖掘压缩序列模式的问题,提出了一个压缩序列模式挖掘算法.该算法通过对闭序列模式全集进行划分处理,降低了序列的比对空间,并结合δ-dominant序列检测机制,有效的挖掘出了压缩序列模式集.实验表明,该算法具有较好的运行效率. This paper researches the problem of mining compressed sequential patterns in a static database. By ranging the closed sequential patterns and using the δ - dominant detecting technique, this paper propose a algorithm which can decrease number of closed sequential patterns, and mining compressed sequential patterns efficiently. The experimental results show that the algorithm has higher accuracy.
作者 谢伙生 黄钧钧
机构地区 福州大学数学与计算机科学学院
出处 《福州大学学报（自然科学版）》 CAS CSCD 北大核心 2012年第4期459-463,共5页 Journal of Fuzhou University(Natural Science Edition)
基金 福州大学科技发展基金资助项目(2006-XQ-22) 福建省教育厅科研资助项目(JB07023)
关键词 闭序列模式 压缩序列模式 数据挖掘 closed frequent sequential patterns compressed sequential patterns data mining
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献11

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二级参考文献25

  • 1宋世杰,胡华平,周嘉伟,金士尧.一种基于大项集重用的序列模式挖掘算法[J].计算机研究与发展,2006,43(1):68-74. 被引量：10
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共引文献10

福州大学学报（自然科学版）
2012年 第4期

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