一种面向多属性不确定数据流的模体发现算法

王菊; 刘付显

doi:10.11999/JEIT160247

一种面向多属性不确定数据流的模体发现算法

doi: 10.11999/JEIT160247

王菊^1,,
刘付显¹

基金项目:

国家自然科学基金(61272011)

计量
- 文章访问数: 1320
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- PDF下载量: 497
- 被引次数: 0
出版历程
- 收稿日期: 2016-03-17
- 修回日期: 2016-08-16
- 刊出日期: 2017-01-19

Motif Discovery Algorithm for Multiple Attributes Uncertain Data Stream

WANG Ju^1
,,
LIU Fuxian¹

Funds:

The National Natural Science Foundation of China (61272011)

摘要

摘要: 该文针对多属性不确定数据流的频繁模式发现问题，借鉴生物信息学中的模体发现思想，提出了一种基于MEME(Multiple Expectation-maximization for Motif Elicitation)的多属性不确定数据流模体发现算法。该算法根据不确定数据流的特点，设计了基于混合型模型的不确定滑动窗口更新计算方法，改进了SAX(Symbolic Aggregate approXimation)的符号化策略，提出了不同滑动窗口下多属性模体的相似性分析方法。在实验当中，用防空反导情报传感器网络中的一组不确定数据流验证了其功能，通过植入不同数目的模体测试了其发现准确率，并在元组有效概率设置为1的条件下与已有算法进行了比较，结果表明：该算法可以较准确地发现多属性不确定数据流中的频繁模式。
- 数据挖掘 /
- 模体发现 /
- 不确定数据流 /
- MEME(Multiple Expectation-maximization for Motif Elicitation)算法
Abstract: Algorithm of motif discovery for multiple attributes uncertain data stream is proposed on the basis of MEME (Multiple Expectation-maximization for Motif Elicitation), which consults the thought of sequential pattern discovery in bioinformatics to solve the problem of frequent pattern discovery for multiple attributes uncertain data stream. A new method for update calculation of uncertain sliding window is designed based on mixed type model, SAX (Symbolic Aggregate approXimation) symbolic strategy is improved, and similarity analysis method for multiple attributes motifs under different sliding windows is put forward. The proposed algorithm is verified to be correct functionally by a set of uncertain data stream in the wireless sensor network of air and missile defense. Its accuracy is measured through planting different number of motifs. Furthermore, comparison with previous algorithm with tuples valid probability set to 1 shows that the proposed algorithm can discover frequent pattern for multiple attributes uncertain data stream precisely.
- Data mining /
- Motif discovery /
- Uncertain data stream; Multiple Expectation-maximization for Motif Elicitation (MEME) algorithm /

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参考文献(30)

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