Algorithms for detecting outliers via clustering and ranks

Authors:
Huaming Huang;Kishan Mehrotra;Chilukuri K. Mohan
Affiliations:
Department of EECS, Syracuse University;Department of EECS, Syracuse University;Department of EECS, Syracuse University
Venue:
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Year:
2012

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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.