by Simon Hawkins, Hongxing He, Graham Williams, Rohan Baxter
In Proc. of the Fifth Int. Conf. and Data Warehousing and Knowledge Discovery (DaWaK02
http://www.act.cmis.csiro.au/rohanb/PAPERS/dawak02.pdf
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Abstract:
Abstract. We consider the problem of finding outliers in large multivariate databases. Outlier detection can be applied during the data cleansing process of data mining to identify problems with the data itself, and to fraud detection where groups of outliers are often of particular interest. We use replicator neural networks (RNNs) to provide a measure of the outlyingness of data records. The performance of the RNNs is assessed using a ranked score measure. The effectiveness of the RNNs for outlier detection is demonstrated on two publicly available databases. 1
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