| 摘要: |
| 研究了数据流中频繁模式的挖掘问题，主要贡献在于：(1)基于启发式思想方法和抽样理论的基础上，提出了基于数据流样本集的分步模式估计方法；(2)算法求解所有长度的模式，而不仅仅是单项集模式；(3)为了找到满足精度要求的恰当的数据流样本集长度，引入了Hoeffding bound理论，并进行了修正，从而使之更适合于这一问题：(4) 提出了对估计模式进行在线维护的方法．基于上述方法的基础上，提出了模式估计和维护算法．最后，通过和已有算法进行实验对比分析，结果表明，该算法在结果精度、空间、时间复杂性等方面都适合进行数据流的分析． |
| 关键词: 数据流挖掘 抽样 频繁模式 Hoeffding bounds 启发式方法 |
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| 基金项目:Supported by the National Grand Fundamental Research 973 Program of China under Grant No．G1999032705(国家重点基础研究发展规划(973)) |
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| Estimation and Maintenance of Frequent Pattern on Data Streams |
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SONG Guo-Jie,TANG Shi-Wei,YANG Dong-Qing,WANG Teng-Jiao
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| Abstract: |
| In this paper, the methods are investigate for online，frequent paRem mining of stream data,with the following contributions：(1) based on heuristic methodology and sample theory,step-by-step data stream mining method is used to estimate potential paRern set；(2)will find any length paRern not only single item pattern；(3)to find more appropriate length of each segment satisfying accuracy requirement,Hoeffding bound theory was introduced and revised to make it more suit for pattern mining；(4)a maintenance approach for estimating frequent patterns is developed for on．1ine analysis．Based on this design，estimation and maintenance algorithms are proposed for efficient analysis of data streams．This performance study compares the proposed algorithms and identifies the most accuracy-，memory-and time-efficient algorithms for stream data analysis． |
| Key words: data streammining sample frequentpattern Hoeffdingbounds：heuristicmethod |
