曲文龙, 杨炳儒, 张克君. 基于广义后缀树的事件序列频繁情节挖掘算法[J]. 工程科学学报, 2006, 28(5): 490-496. DOI: 10.13374/j.issn1001-053x.2006.05.039
引用本文: 曲文龙, 杨炳儒, 张克君. 基于广义后缀树的事件序列频繁情节挖掘算法[J]. 工程科学学报, 2006, 28(5): 490-496. DOI: 10.13374/j.issn1001-053x.2006.05.039
QU Wenlong, YANG Bingru, ZHANG Kejun. Mining algorithm of frequent episodes in an event sequence based on generalized suffix-tree[J]. Chinese Journal of Engineering, 2006, 28(5): 490-496. DOI: 10.13374/j.issn1001-053x.2006.05.039
Citation: QU Wenlong, YANG Bingru, ZHANG Kejun. Mining algorithm of frequent episodes in an event sequence based on generalized suffix-tree[J]. Chinese Journal of Engineering, 2006, 28(5): 490-496. DOI: 10.13374/j.issn1001-053x.2006.05.039

基于广义后缀树的事件序列频繁情节挖掘算法

Mining algorithm of frequent episodes in an event sequence based on generalized suffix-tree

  • 摘要: 为了有效地挖掘事件序列频繁情节,提出了一种广义后缀树结构发现和存储频繁情节.此结构利用广义后缀概念并且树中只包含频繁情节结点,用频繁情节发生列表逐层构建的方法提高了建树效率.该方法充分利用了事件序列的有序特点,可用于发现各类频繁情节.实验结果表明该算法性能优于Apriori-like频繁情节发现算法.

     

    Abstract: In order to mine frequent episodes from an event sequence efficiently, an algorithm based on generalized suffix-tree was proposed to discover and store frequent episodes, which uses the concept of generalized suffix and contains only frequent episodes' nodes. The occurrence list of frequent episodes was used layer-upon-layer to improve the efficiency of the tree. The algorithm make full use of the order character of an event sequence and may discover the variety of frequent episodes. Experimental results show that the proposed algorithm is superior in runtime to Apriori-like frequent episodes mining algorithm.

     

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