A Fast Outlier Detection Algorithm for High Dimensional Categorical Data Streams
| Autor: | Zhang Baili, Yang Yidong, Zhou Xiao-Yun, Zhou Xy, Sun Zhihui |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Data stream
Measure (data warehouse) Concept drift Data stream mining business.industry Computer science Pattern recognition computer.software_genre Data set ComputingMethodologies_PATTERNRECOGNITION Computer Science::Multimedia Outlier Metric (mathematics) Anomaly detection Artificial intelligence Data mining business Categorical variable computer Algorithm Software |
| Zdroj: | Journal of Software. 18:933 |
| ISSN: | 1000-9825 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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