Abstract
Outlier detection has many practical applications, especially in domains that have scope for abnormal behavior, such as fraud detection, network intrusion detection, medical diagnosis, etc. In this paper, we present a technique for detecting outliers and learning from data in multi-dimensional streams. Since the concept in such streaming data may drift, learning approaches should be online and should adapt quickly. Our technique adapts to new incoming data points, and incrementally maintains the models it builds in order to overcome the effect of concept drift. Through various experimental results on real data sets, our approach is shown to be effective in detecting outliers in data streams as well as in maintaining model accuracy.
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Hoang Vu, N., Gopalkrishnan, V., Namburi, P. (2008). Online Outlier Detection Based on Relative Neighbourhood Dissimilarity. In: Bailey, J., Maier, D., Schewe, KD., Thalheim, B., Wang, X.S. (eds) Web Information Systems Engineering - WISE 2008. WISE 2008. Lecture Notes in Computer Science, vol 5175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85481-4_6
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DOI: https://doi.org/10.1007/978-3-540-85481-4_6
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