Abstract
Rank-based algorithms provide a promising approach for outlier detection, but currently used rank-based measures of outlier detection suffer from two deficiencies: first they assign a large value to an object near a cluster whose density is high even through the object may not be an outlier and second the distance between the object and its nearest cluster plays a mild role though its rank with respect to its neighbor. To correct for these deficiencies we introduce the concept of modified-rank and propose new algorithms for outlier detection based on this concept. Our method performs better than several density-based methods, on some synthetic data sets as well as on some real data sets.
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Huang, H., Mehrotra, K., Mohan, C.K. (2012). Algorithms for Detecting Outliers via Clustering and Ranks. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_3
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DOI: https://doi.org/10.1007/978-3-642-31087-4_3
Publisher Name: Springer, Berlin, Heidelberg
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