Journal article

Mining outlying aspects on numeric data

Lei Duan, Guanting Tang, Jian Pei, James Bailey, Akiko Campbell, Changjie Tang

Data Mining and Knowledge Discovery | SPRINGER | Published : 2015

Abstract

When we are investigating an object in a data set, which itself may or may not be an outlier, can we identify unusual (i.e., outlying) aspects of the object? In this paper, we identify the novel problem of mining outlying aspects on numeric data. Given a query object o in a multidimensional numeric data set O, in which subspace is o most outlying? Technically, we use the rank of the probability density of an object in a subspace to measure the outlyingness of the object in the subspace. A minimal subspace where the query object is ranked the best is an outlying aspect. Computing the outlying aspects of a query object is far from trivial. A naïve method has to calculate the probability densit..

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Grants

Awarded by Natural Science Foundation of China


Awarded by China Postdoctoral Science Foundation


Awarded by ARC Future Fellowship


Funding Acknowledgements

The authors thank the editor and the anonymous reviewers for their invaluable comments, which help to improve this paper. Lei Duan's research is supported in part by Natural Science Foundation of China (Grant No. 61103042), China Postdoctoral Science Foundation (Grant No. 2014M552371). Work by Lei Duan at Simon Fraser University was supported in part by an Ebco/Eppich visiting professorship. Jian Pei's and Guanting Tang's research is supported in part by an NSERC Discovery grant, a BCIC NRAS Team Project. James Bailey's work is supported by an ARC Future Fellowship (FT110100112). All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.