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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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References

  • Aggarwal CC, Yu PS (2001) Outlier detection for high dimensional data. In Proceedings of the 2001 ACM SIGMOD international conference on management of data, SIGMOD ’01, ACM, New York, pp 37–46

  • Bache K, Lichman M (2013) UCI machine learning repository. University of California, Irvine

    Google Scholar 

  • Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. SIGMOD Rec 29(2):93–104

    Article  Google Scholar 

  • Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. The MIT Press, Cambridge

    MATH  Google Scholar 

  • Dang X, Micenkova B, Assent I, Ng R (2013) Local outlier detection with interpretation. In: Blockeel H, Kersting K, Nijssen S, Elezn F (eds) Machine learning and knowledge discovery in databases, vol 8190., Lecture notes in computer scienceSpringer, Berlin, pp 304–320

    Chapter  Google Scholar 

  • Dang XH, Assent I, Ng RT, Zimek A, Schubert E (2014) Discriminative features for identifying and interpreting outliers. In Proceedings of the IEEE 30th international conference on data engineering (ICDE), pp 88–99

  • Duan L, Tang G, Pei J, Bailey J, Dong G, Campbell A, Tang C (2014) Mining contrast subspaces. In: Tseng V, Ho T, Zhou Z-H, Chen A, Kao H-Y (eds) Advances in knowledge discovery and data mining, vol 8443., Lecture notes in computer scienceSpringer International Publishing, Berlin, pp 249–260

    Chapter  Google Scholar 

  • Duan L, Tang G, Pei J, Bailey J, Campbell A, Tang C (2015) Mining outlying aspects on numeric data. Data Min Knowl Discov 29(5):1116–1151

    Article  MathSciNet  Google Scholar 

  • Garfinkel S, Spafford G, Schwartz A (2003) Practical unix & internet security, 3rd edn. O’Reilly Media Inc, California

    Google Scholar 

  • He Z, Xu X, Huang ZJ, Deng S (2005) Fp-outlier: frequent pattern based outlier detection. Comput Sci Inform Syst 2(1):103–118

    Article  Google Scholar 

  • Keller F, Muller E, Bohm K (2012) HiCS: high contrast subspaces for density-based outlier ranking. In Proceedings of the 2012 IEEE 28th international conference on data engineering, ICDE ’12, IEEE Computer Society, Washington, pp 1037–1048

  • Kriegel H-P, Schubert M, Zimek A (2008) Angle-based outlier detection in high-dimensional data. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’08, ACM, New York, pp 444–452

  • Kriegel H-P, Kruger P, Schubert E, Zimek A (2009) Outlier detectionin axis-parallel subspaces of high dimensional data. In: Theeramunkong T, Kijsirikul B, Cercone N, Ho T-B (eds) Advances in knowledge discovery and data mining, vol 5476., Lecture notes in computer scienceSpringer, Berlin, pp 831–838

    Chapter  Google Scholar 

  • Liu F, Ting KM, Zhou Z-H (2008) Isolation forest. In Proceedings of the 8th IEEE international conference on data mining, ICDM ’08., pp 413–422

  • Liu FT, Ting KM, Zhou Z-H (2012) Isolation-based anomaly detection. ACM Trans Knowl Discov Data 6(1):3:1–3:39

    Article  Google Scholar 

  • Micenkova B, Dang X-H, Assent I, Ng R (2013) Explaining outliers by subspace separability. In Proceedings of the 2013 IEEE 13th international conference on data mining (ICDM), pp 518–527

  • Nguyen HV, Müller E, Vreeken J, Keller F, Böhm K (2013) CMI: an information-theoretic contrast measure for enhancing subspace cluster and outlier detection. In Proceedings of the 2013 SIAM data mining conference (SDM), pp 198–206

  • Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  • Romano S, Bailey J, Vinh NX, Verspoor K (2014) Standardized mutual information for clustering comparisons: One step further in adjustment for chance. In T. Jebara and E. P. Xing (eds) Proceedings of the 31st international conference on machine learning (ICML-14), pp 1143–1151

  • Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Pearson Education, London

    MATH  Google Scholar 

  • Sabhnani M, Serpen G (2003) KDD feature set complaint heuristic rules for R2L attack detection. In Proceedings of the international conference on security and management, SAM ’03, Vol 1, Las Vegas, 23–26 June 2003, pp 310–316

  • Smets K, Vreeken J (2011) The odd one out: Identifying and characterising anomalies. In Proceedings of the 2011 SIAM international conference on data mining, chapter 69, pp 804–815

  • Vinh NX, Epps J, Bailey J (2010) Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J Mach Learn Res 11:2837–2854

    MathSciNet  MATH  Google Scholar 

  • Vinh NX, Chan J, Romano S, Bailey J (2014a) Effective global approaches for mutual information based feature selection. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14, ACM, New York, pp 512–521

  • Vinh NX, Chan J, Bailey J (2014b) Reconsidering mutual informationbased feature selection: a statistical significance view. In Proceedings of the twenty-eighth AAAI conference on artificialintelligence, Québec City, 27 -31 July 2014, pp 2092–2098

  • Wu T, Xin D, Mei Q, Han J (2009) Promotion analysis in multi-dimensional space. Proc VLDB Endow 2(1):109–120

    Article  Google Scholar 

  • Zhang J, Lou M, Ling TW, Wang H (2004) Hos-miner: a system for detecting outlyting subspaces of high-dimensional data. In Proceedings of the thirtieth international conference on very large data bases , Vol 30, VLDB ’04, VLDB Endowment, Brussels, pp 1265–1268

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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

Authors
  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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