| 摘要: |
| 提出类别属性数据流数据离群度量--加权频繁模式离群因子(weighted frequent pattern outlier factor,简称WFPOF),并在此基础上给出一种快速数据流离群点检测算法FODFP-Stream(fast outlier detection for high dimensional categorical data streams based on frequent pattern).该算法通过动态发现和维护频繁模式来计算离群度,能够有效地处理高维类别属性数据流,并可进一步扩 |
| 关键词: 数据流 离群点检测 频繁模式 高维 概念转移 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.70371015(国家自然科学基金);the Doctor Science Research Foundation of the Education Ministry of China under Grant No.20040286009(国家教育部高等学校博士学科点科研基金) |
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| A Fast Outlier Detection Algorithm for High Dimensional Categorical Data Streams |
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ZHOU Xiao-Yun,SUN Zhi-Hui,ZHANG Bai-Li,YANG Yi-Dong
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| Abstract: |
| This paper considers the problem of outlier detection in data stream, proposes a new metric called weighted frequent pattern outlier factor for categorical data streams, and presents a novel fast outlier detection algorithm named FODFP-Stream (fast outlier detection for high dimensional categorical data streams based on frequent pattern). FODFP-Stream computes the outlier measure through discovering and maintaining the frequent patterns dynamically, and can deal with the high dimensional categorical data streams effectively. FODFP-Stream can also be extended to resolve continuous attributes and mixed attributes data streams. The experimental results on synthetic and real data sets show the promising availabilities of the approaches. |
| Key words: data stream outlier detection frequent pattern high dimension concept drift |
