基于最小且非重叠发生的频繁闭情节挖掘  

Frequent Closed Episode Mining Based on Minimal and Non-Overlapping Occurrences

作　　者：朱辉生[1,2] 汪卫[2] 施伯乐[2] 

机构地区：[1]泰州师范高等专科学校,江苏泰州225300 [2]复旦大学计算机科学技术学院,上海200433

出　　处：《计算机研究与发展》2013年第4期852-860,共9页Journal of Computer Research and Development

基　　金：国家"九七三"重点基础研究发展计划基金项目(2005CB321905);国家自然科学基金项目(61003001;61103009)

摘　　要：事件序列上的频繁闭情节挖掘是一个重要课题,现有的研究基于最小发生的支持度定义和广度优先的搜索策略,不可避免地导致了情节发生的"过计数"和大量候选情节的产生问题,因此,基于最小且非重叠发生的支持度定义和深度优先的搜索策略,提出了一个事件序列上的频繁闭情节挖掘算法FCEMiner,在此基础上,利用特殊前向扩展的非闭一致性避免了冗余的闭合性检查,缩小了频繁闭情节的搜索空间.理论分析和实验评估证明FCEMiner能够有效地发现事件序列上的频繁闭情节.Mining frequent closed episodes from an event sequence is an important task. The existing research work is based on the support definition of minimal occurrences and the breadth-first search strategy, which unavoidably leads to the issues such as over-counting the occurrences of an episode and generating a huge number of candidate episodes. In this paper, a novel algorithm FCEMiner is proposed to mine frequent closed episodes from an event sequence, which employs the support definition of both minimal and non-overlapping occurrences and the depth-first search strategy. Moreover, FCEMiner utilizes the non-closed unanimity of special forward extension to skip redundant closure checking and narrow down the search space of frequent closed episodes. Both theoretical study and experimental evaluation confirm that FCEMiner is able to effectively discover frequent closed episodes from an event sequence.

关 键 词：事件序列 频繁闭情节 最小且非重叠发生 深度优先 数据挖掘 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]