Abstract
There has been increasing interest in deploying data mining methods for fault detection. For the case where we have potentially large numbers of devices to monitor, we propose to use peer group analysis to identify faults. First, we identify the peer group of each device. This consists of other devices that have behaved similarly. We then monitor the behaviour of a device by measuring how well the peer group tracks the device. Should the device’s behaviour deviate strongly from its peer group we flag the behaviour as an outlier. An outlier is used to indicate the potential occurrence of a fault. A device exhibiting outlier behaviour from its peer group need not be an outlier to the population of devices. Indeed a device exhibiting behaviour typical for the population of devices might deviate sufficiently far from its peer group to be flagged as an outlier. We demonstrate the usefulness of this property for detecting faults by monitoring the data output from a collection of privately run weather stations across the UK.
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Acknowledgements
We would like to express appreciation to Weather Underground, Inc. for use of their data. The work of David Weston was supported by grant number EP/C532589/1 from the UK Engineering and Physical Sciences Research Council. The work of Yoonseong Kim was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2006-612-D00100). The work of David Hand was partially supported by a Royal Society Wolfson Research Merit Award.
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© 2012 Springer-Verlag Berlin Heidelberg
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Weston, D.J., Adams, N.M., Kim, Y., Hand, D.J. (2012). Fault Mining Using Peer Group Analysis. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_46
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DOI: https://doi.org/10.1007/978-3-642-24466-7_46
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