数据流的突发变化检测算法

Xian-fei Yang, Jian-pei Zhang, Yang Jing, Li Xiang
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引用次数: 2

摘要

概率分布在任意时刻的变化是数据流的一个重要特征。当这种情况发生时,数据挖掘算法必须适应新的概率分布。因此,如何检测数据流中的突发变化是数据流挖掘的重要组成部分。本文利用hoeffding定理和独立同分布中心极限定理,提出了一种BCDADS检测算法。理论和实验表明,该算法能有效检测数据流中的突发变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A burst change detection algorithm for data streams
Burst Change of probability distribution at any moment is an important characteristic in data streams. When it has happened, data mining algorithm must adapt itself to new probability distribution. So how to detect burst change in data streams is an important part of data stream mining. In this paper, we proposed an algorithm BCDADS to detect it by using hoeffding theorem and independent identical distribution central limit theorem. Theory and experiment indicated this algorithm can effectively detect burst change in data streams.
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