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Info-Detection: An Information-Theoretic Approach to Detect Outlier

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Neural Information Processing (ICONIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1143))

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Abstract

Outlier detection is one of major task in unsupervised learning. We propose a cluster analysis based outlier detection method called Info-Detection. Info-Detection determines the number of outliers automatically and captures the global property of the provided data. To implement Info-Detection and overcome the global computational complexity, we use principal sequence of partition, which we improve one order of magnitude faster than the original version. Experiments show that compared with other outlier detection methods, Info-Detection achieves better accuracy with an affordable time overhead.

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References

  1. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. SIGMOD Reco. 29(2), 93–104 (2000)

    Article  Google Scholar 

  2. Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J.: Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans. Knowl. Discov. Data 10(1), 5:1–5:51 (2015)

    Article  Google Scholar 

  3. Chan, C., Al-Bashabsheh, A., Zhou, Q., Kaced, T., Liu, T.: Info-clustering: a mathematical theory for data clustering. IEEE T-MBMC 2(1), 64–91 (2016)

    Google Scholar 

  4. Grubbs, F.E.: Procedures for detecting outlying observations in samples. Technometrics 11(1), 1–21 (1969)

    Article  Google Scholar 

  5. Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)

    Article  Google Scholar 

  6. Kolmogorov, V.: A faster algorithm for computing the principal sequence of partitions of a graph. Algorithmica 56(4), 394–412 (2010)

    Article  MathSciNet  Google Scholar 

  7. Liu, F.T., Ting, K.M., Zhou, Z.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422, December 2008

    Google Scholar 

  8. Nagano, K., Kawahara, Y., Iwata, S.: Minimum average cost clustering. In: NIPS 23, pp. 1759–1767. Curran Associates, Inc. (2010)

    Google Scholar 

  9. Narayanan, H.: The principal lattice of partitions of a submodular function. Linear Algebra Appl. 144, 179–216 (1991)

    Article  MathSciNet  Google Scholar 

  10. Rousseeuw, P.J., Driessen, K.V.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3), 212–223 (1999)

    Article  Google Scholar 

  11. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution (2001)

    Google Scholar 

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Acknowledgment

The research of Shao-Lun Huang was funded by the Natural Science Foundation of China 61807021, Shenzhen Science and Technology Research and Development Funds (JCYJ20170818094022586), and Innovation and entrepreneurship project for overseas high-level talents of Shenzhen (KQJSCX20180327144037831).

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Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Feng Zhao & Lin Zhang

  2. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Beijing, China

    Fei Ma, Yang Li, Shao-Lun Huang & Lin Zhang

Authors
  1. Feng Zhao
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  2. Fei Ma
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  3. Yang Li
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  4. Shao-Lun Huang
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  5. Lin Zhang
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Corresponding author

Correspondence to Feng Zhao .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Zhao, F., Ma, F., Li, Y., Huang, SL., Zhang, L. (2019). Info-Detection: An Information-Theoretic Approach to Detect Outlier. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_52

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  • DOI: https://doi.org/10.1007/978-3-030-36802-9_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36801-2

  • Online ISBN: 978-3-030-36802-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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