caa a22 4500 467952094 CHVBK 20180405193743.0 cr unu---uuuuu 170328e20041001xx s 000 0 eng 10.1007/s10462-004-4304-y doi (NATIONALLICENCE)springer-10.1007/s10462-004-4304-y (NATIONALLICENCE)springer-10.1023/B:AIRE.0000045502.10941.a9 A Survey of Outlier Detection Methodologies [Elektronische Daten] [Victoria Hodge, Jim Austin] Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review. Kluwer Academic Publishers, 2004 anomaly nationallicence detection nationallicence deviation nationallicence noise nationallicence novelty nationallicence outlier nationallicence recognition nationallicence Hodge Victoria Department of Computer Science, University of York, YO10 5DD, York, UK aut Austin Jim Department of Computer Science, University of York, YO10 5DD, York, UK aut Artificial Intelligence Review Kluwer Academic Publishers 22/2(2004-10-01), 85-126 0269-2821 22:2<85 2004 22 10462 https://doi.org/10.1007/s10462-004-4304-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10462-004-4304-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Hodge Victoria Department of Computer Science, University of York, YO10 5DD, York, UK aut NATIONALLICENCE 700 1- Austin Jim Department of Computer Science, University of York, YO10 5DD, York, UK aut NATIONALLICENCE 773 0- Artificial Intelligence Review Kluwer Academic Publishers 22/2(2004-10-01), 85-126 0269-2821 22:2<85 2004 22 10462 NATIONALLICENCE 700 1- Hodge Victoria Department of Computer Science, University of York, UK aut NATIONALLICENCE 700 1- Austin Jim Department of Computer Science, University of York, UK aut NATIONALLICENCE 856 40 https://doi.org/10.1023/B:AIRE.0000045502.10941.a9 text/html Onlinezugriff via DOI Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE NATIONALLICENCE NL-springer