@inproceedings{d3309896be4348f9964edfe49d6d6009,
title = "Algorithms for detecting outliers via clustering and ranks",
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.",
keywords = "Outlier detection, clustering, neighborhood sets, ranking",
author = "Huaming Huang and Kishan Mehrotra and Mohan, {Chilukuri K.}",
year = "2012",
doi = "10.1007/978-3-642-31087-4_3",
language = "English (US)",
isbn = "9783642310867",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "20--29",
booktitle = "Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings",
note = "25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012 ; Conference date: 09-06-2012 Through 12-06-2012",
}