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Published Articles >> Table of Contents >> Abstract
19th International Conference on Data Engineering (ICDE'03)
p. 315
LOCI: Fast Outlier Detection Using the Local Correlation Integral
Spiros Papadimitriou, Carnegie Mellon University
Hiroyuki Kitagawa, University of Tsukuba
Phillip B. Gibbons, Intel Research Pittsburgh
Christos Faloutsos, Carnegie Mellon University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2003.1260802
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Outlier detection is an integral part of data mining and has attracted much attention recently [8, 15, 19]. In this paper, we propose a new method for evaluating outlier-ness, which we call the Local Correlation Integral (LOCI). As with the best previous methods, LOCI is highly effective for detecting outliers and groups of outliers (a.k.a. micro-clusters). In addition, it offers the following advantages and novelties: (a) It provides an automatic, data-dictated cut-off to determine whether a point is an outlier-in contrast, previous methods force users to pick cut-offs, without any hints as to what cut-off value is best for a given dataset. (b) It can provide a LOCI plot for each point; this plot summarizes a wealth of information about the data in the vicinity of the point, determining clusters, micro-clusters, their diameters and their inter-cluster distances. None of the existing outlier-detection methods can match this feature, because they output only a single number for each point: its outlier-ness score. (c) Out LOCI method can be computed as quickly as the best previous methods. (d) Moreover, LOCI leads to a practically linear approximate method, aLOCI (for approximate LOCI), which provides fast highly-accurate outlier detection. To the best of our knowledge, this is the first work to use approximate computations to speed up outlier detection.
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Citation:
Spiros Papadimitriou, Hiroyuki Kitagawa, Phillip B. Gibbons, Christos Faloutsos,
"LOCI: Fast Outlier Detection Using the Local Correlation Integral,"
icde,
p. 315,
19th International Conference on Data Engineering (ICDE'03),
2003
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