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Discovering outlying aspects in large datasets

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Abstract

We address the problem of outlying aspects mining: given a query object and a reference multidimensional data set, how can we discover what aspects (i.e., subsets of features or subspaces) make the query object most outlying? Outlying aspects mining can be used to explain any data point of interest, which itself might be an inlier or outlier. In this paper, we investigate several open challenges faced by existing outlying aspects mining techniques and propose novel solutions, including (a) how to design effective scoring functions that are unbiased with respect to dimensionality and yet being computationally efficient, and (b) how to efficiently search through the exponentially large search space of all possible subspaces. We formalize the concept of dimensionality unbiasedness, a desirable property of outlyingness measures. We then characterize existing scoring measures as well as our novel proposed ones in terms of efficiency, dimensionality unbiasedness and interpretability. Finally, we evaluate the effectiveness of different methods for outlying aspects discovery and demonstrate the utility of our proposed approach on both large real and synthetic data sets.

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Acknowledgments

This work is supported by the Australian Research Council via Grant Numbers FT110100112 and DP140101969.

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Authors and Affiliations

  1. The University of Melbourne, Melbourne, Australia

    Nguyen Xuan Vinh, Jeffrey Chan, Simone Romano, James Bailey, Christopher Leckie & Kotagiri Ramamohanarao

  2. Simon Fraser University, Burnaby, Canada

    Jian Pei

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  1. Nguyen Xuan Vinh
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  2. Jeffrey Chan
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  3. Simone Romano
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  4. James Bailey
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  5. Christopher Leckie
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  6. Kotagiri Ramamohanarao
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  7. Jian Pei
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Corresponding author

Correspondence to Nguyen Xuan Vinh.

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Responsible editor: Charu Aggarwal.

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Vinh, N.X., Chan, J., Romano, S. et al. Discovering outlying aspects in large datasets. Data Min Knowl Disc 30, 1520–1555 (2016). https://doi.org/10.1007/s10618-016-0453-2

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