An Efficient Data Stream Outliers Detection Algorithm Based on k-Means Partitioning

Ni Weiwei, Lu Jieping, Chen Geng, and Sun Zhihui

Journal of Computer Research and Development > 2006 > 43(9): 1639-1643.

Ni Weiwei, Lu Jieping, Chen Geng, and Sun Zhihui. An Efficient Data Stream Outliers Detection Algorithm Based on k-Means Partitioning[J]. Journal of Computer Research and Development, 2006, 43(9): 1639-1643.

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An Efficient Data Stream Outliers Detection Algorithm Based on k-Means Partitioning

Ni Weiwei, Lu Jieping, Chen Geng, and Sun Zhihui

(College of Computer Science and Engineering, Southeast University, Nanjing 210096)

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Published Date: September 14, 2006

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Abstract

Abstract

Outliers detection is an important issue in data mining. It is difficult to find outliers in data streams because data streams are dynamic, one pass readable and of large amount of data. In this paper, a data stream outliers detection algorithm based on k-means partioning—DSOKP is proposed, which applies k means clustering on each partition of the data stream to generate mean reference point set, and subsequently picks out those potential outliers of each periods according to the definition of outliers. Theoretic analysis and experimental results indicate that DSOKP is effective and efficient.
- data mining,
- outliers detection,
- mean reference point,
- clustering

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