Abstract
The problem of unsupervised outlier detection is challenging, especially when the structure of data is unknown. This paper presents a new density-based outlier detection technique that detects the top-n outliers. It overcomes the limitations of existing approaches, like low accuracy and high sensitivity to parameters. Our approach provides a score to each object called Dynamic-Window Outlier Factor (DWOF). DWOF improves Resolution-based Outlier Factor method (ROF) to consider varying-density clusters, which improves outliers’ ranking even when providing same outliers. Experiments show that DWOF’s average accuracy is better than existing approaches and less sensitive to its parameter.
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Momtaz, R., Mohssen, N., Gowayyed, M.A. (2013). DWOF: A Robust Density-Based Outlier Detection Approach. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_61
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DOI: https://doi.org/10.1007/978-3-642-38628-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
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