RNN在数据挖掘异常点检测中的比较研究

Graham J. Williams, R. Baxter, Hongxing He, S. Hawkins, Lifang Gu
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引用次数: 292

摘要

我们提出了复制神经网络(rnn)用于异常值检测。我们使用公开可用的统计数据集(通常较小)和数据挖掘数据集(通常更大,通常是真实数据)将RNN与其他三种方法进行异常值检测比较。较小的数据集提供了对rnn的相对优势和劣势的见解。较大的数据集尤其考验着应用的可扩展性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparative study of RNN for outlier detection in data mining
We have proposed replicator neural networks (RNNs) for outlier detection. We compare RNN for outlier detection with three other methods using both publicly available statistical datasets (generally small) and data mining datasets (generally much larger and generally real data). The smaller datasets provide insights into the relative strengths and weaknesses of RNNs. The larger datasets in particular test scalability and practicality of application.
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