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基于最大频繁项集信息熵的数据流变化检测 被引量：1

Online Detection of Data Stream Changes Based on Maximum Frequent Itemset Entropy
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摘要 应用最大频繁项集信息熵来进行数据流变化检测.采用了一种新的数据流差异度度量方法;提出了一种新的有效挖掘最大频繁项集的算法;给出了应用最大频繁项集信息熵进行数据流变化检测的算法.最后,对算法的时间效率和空间效率进行了分析. Online detection of data stream changes is a new topic in data stream studies, which provides a salient feature compared to other types of data mining. In this paper, a novel method for detection and estimation of data stream changes is proposed. The main concerns include： 1 ） adoption of a novel discrepancy measure for data streams, 2） a new algorithm which can effectively explore and store all maximum frequent itemsets for data streams, and 3 ） a method for detection of changes based on maximum frequent itemsets information entropy. No previous work has been reported to the authors＇ best knowledge using maximum frequent itemsets entropy model in detecting data stream changes. Experiments were carried out to study temporal and spatial efficiency of the algorithm.
作者 刘学军 徐宏炳 董逸生 钱江波 王永利
机构地区 东南大学计算机科学与技术系
出处 《应用科学学报》 CAS CSCD 北大核心 2006年第5期498-502,共5页 Journal of Applied Sciences
基金 江苏省高技术项目(BG2004034) 江苏省2004年度研究生创新计划项目(xm04-36)
关键词 数据流 最大频繁项集 变化检测 数据流分析 data stream maximum frequent itemsets change detection data stream analysis
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献11

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共引文献18

同被引文献29

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2006年 第5期

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