Outlier Detection Using Replicator Neural Networks
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 replicato...
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creator | Hawkins, Simon He, Hongxing Williams, Graham Baxter, Rohan |
description | 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. |
doi_str_mv | 10.1007/3-540-46145-0_17 |
format | Conference Proceeding |
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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. 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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.</description><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Fraud Detection</subject><subject>Hide Layer</subject><subject>Information systems. Data bases</subject><subject>Memory organisation. Data processing</subject><subject>Network Intrusion Detection</subject><subject>Outlier Detection</subject><subject>Outlier Detection Method</subject><subject>Software</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540441239</isbn><isbn>9783540441236</isbn><isbn>9783540461456</isbn><isbn>3540461450</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2002</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkMtLAzEYxOMLbGvvHvfiMfXLY5PNUeoTigWx5_BtNilr192SrIj_vbH1NDAzDMOPkGsGCwagbwUtJVCpmCwpWKZPyNzoSmTz4KlTMmGKMSqENGdkeggk48KckwkI4NRoKS7JNKUPAODa8Anh66-xa30s7v3o3dgOfbFJbb8t3vy-ax2OQyxe_VfELsv4PcRduiIXAbvk5_86I5vHh_flM12tn16WdyvqeMl1_sobBF2rUKFTUpoAgE1VVkEAq0tj6obz4DgiFyJA0AANotJc1xUy5cSM3Bx395gcdiFi79pk97H9xPhjmawqVoLKvcWxl3LUb3209TDskmWZUcZmhc0g7AGR_cMmfgHKUFm6</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Hawkins, Simon</creator><creator>He, Hongxing</creator><creator>Williams, Graham</creator><creator>Baxter, Rohan</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2002</creationdate><title>Outlier Detection Using Replicator Neural Networks</title><author>Hawkins, Simon ; He, Hongxing ; Williams, Graham ; Baxter, Rohan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2527-542da07b6f8ac6449f00ad858f301b599bd22fc2aa233f0f700daa6727b8a16c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Fraud Detection</topic><topic>Hide Layer</topic><topic>Information systems. 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Data processing</topic><topic>Network Intrusion Detection</topic><topic>Outlier Detection</topic><topic>Outlier Detection Method</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hawkins, Simon</creatorcontrib><creatorcontrib>He, Hongxing</creatorcontrib><creatorcontrib>Williams, Graham</creatorcontrib><creatorcontrib>Baxter, Rohan</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hawkins, Simon</au><au>He, Hongxing</au><au>Williams, Graham</au><au>Baxter, Rohan</au><au>Winiwarter, Werner</au><au>Kambayashi, Yahiko</au><au>Arikawa, Masatoshi</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Outlier Detection Using Replicator Neural Networks</atitle><btitle>Data Warehousing and Knowledge Discovery</btitle><date>2002</date><risdate>2002</risdate><spage>170</spage><epage>180</epage><pages>170-180</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540441239</isbn><isbn>9783540441236</isbn><eisbn>9783540461456</eisbn><eisbn>3540461450</eisbn><abstract>We consider the problem of finding outliers in large multivariate databases. 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source | Springer Books |
subjects | Applied sciences Computer science control theory systems Exact sciences and technology Fraud Detection Hide Layer Information systems. Data bases Memory organisation. Data processing Network Intrusion Detection Outlier Detection Outlier Detection Method Software |
title | Outlier Detection Using Replicator Neural Networks |
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