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《Journal of Software》 2007-04
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A Fast Outlier Detection Algorithm for High Dimensional Categorical Data Streams

ZHOU Xiao-Yun+, SUN Zhi-Hui, ZHANG Bai-Li, YANG Yi-Dong (Department of Computer Science and Engineering, Southeast University, Nanjing 210096, China)  
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.
【Fund】： SupportedbytheNationalNaturalScienceFoundationofChinaunderGrantNo.70371015(国家自然科学基金);; theDoctorScienceResearchFoundationoftheEducationMinistryofChinaunderGrantNo.20040286009(国家教育部高等学校博士学科点科研基金)
【CateGory Index】： TP311.13
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