关键词: 概念漂移, 数据流, K-means聚类, 可变滑动窗口, 离群点检测

Abstract: Aiming at the problem of outlier mining in data streams, an outlier detection algorithm, DOKM algorithm, based on the K-means clustering algorithm, with distance-based criterion to evaluate the issues of outliers is proposed in this paper. For making the detection result of the concept drift of data stream it automatically adjusts the sliding window size and detects outliers in the data stream. A set of experiments show that the proposed DOKM algorithm can achieve the effective detection of outliers in both an artificial data set and a real data set compared with other outlier detection algorithms.

Key words: concept drift, data stream, K-means clustering, variable sliding window, outlier detection