Abstract
We present in this paper a new measure named GOF (Group Outlier Factor) for cluster outliers and novelty detection. The main difference between GOF and existing methods is that being an outlier is not associated to a single pattern but to a cluster. GOF is based on relative density of each group of data and provides a quantitative indicator of outlier-ness which enables to detect automatically ”cluster outliers”. To learn GOF measure, we integrate it in a clustering process using Self-organizing Map. Experimental results and comparison studies show that the use of GOF sensibly improves the results in term of cluster-outlier detection and novelty detection.
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Chaibi, A., Lebbah, M., Azzag, H. (2012). Novelty Detection Using a New Group Outlier Factor. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_45
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DOI: https://doi.org/10.1007/978-3-642-34487-9_45
Publisher Name: Springer, Berlin, Heidelberg
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