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首页> 外文会议>Proceedings of the 2006 International Conference on Machine Learning and Cybernetics >ODABK: AN EFFECTIVE APPROACH TO DETECTING OUTLIER IN DATA STREAM
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ODABK: AN EFFECTIVE APPROACH TO DETECTING OUTLIER IN DATA STREAM

机译：ODABK：一种检测数据流中异常点的有效方法

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摘要

Currently, data mining in data stream becomes a very popular research field. One of the central tasks in mining data streams is that of identifying outliers which can lead to discovering unexpected and interesting knowledge, which is critical important. To effectively mine outliers in data stream,ODABK, an algorithm for outlier detection in data stream is presented. It is based on KNN and significantly enhanced by means of other data structures and its optimized logical operations. Finally, the paper reports experiments on a real-world census data which show that ODABK is more effective in detection rate and execution times.
机译：当前，数据流中的数据挖掘已成为非常流行的研究领域。挖掘数据流的中心任务之一是识别异常值，这可能导致发现意外和有趣的知识，这一点至关重要。为了有效地挖掘数据流中的离群值ODABK，提出了一种用于数据流中离群值检测的算法。它基于KNN，并通过其他数据结构及其优化的逻辑运算得到了显着增强。最后，本文报告了对真实普查数据的实验，结果表明ODABK在检测率和执行时间上更为有效。

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