Surprise Detection in Multivariate Astronomical Data

  • Kirk D. BorneEmail author
  • Arun Vedachalam
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 902)


Astronomers systematically study the sky with large sky surveys. A common feature of modern sky surveys is that they produce hundreds of terabytes (TB) up to 100 (or more) petabytes (PB) both in the image data archive and in the object catalogs. For example, the LSST will produce a 20–40 PB catalog database. Large sky surveys have enormous potential to enable countless astronomical discoveries. Such discoveries will span the full spectrum of statistics: from rare one-in-a-billion (or one-in-a-trillion) object types, to complete statistical and astrophysical specifications of many classes of objects (based upon millions of instances of each class). The growth in data volumes requires more effective knowledge discovery and extraction algorithms. Among these are algorithms for outlier (novelty/surprise/anomaly) detection. Outlier detection algorithms enable scientists to discover the most “interesting” scientific knowledge hidden within large and high-dimensional datasets: the “unknown unknowns”. Effective outlier detection is essential for rapid discovery of potentially interesting and/or hazardous events. Emerging unexpected conditions in hardware, software, or network resources need to be detected, characterized, and analyzed as soon as possible for obvious system health and safety reasons, just as emerging behaviors and variations in scientific targets should be similarly detected and characterized promptly in order to enable rapid decision support in response to such events. We have developed a new algorithm for outlier detection (KNN-DD: K-Nearest Neighbor Data Distributions). We have derived results from preliminary experiments in terms of the algorithm’s precision and recall for known outliers, and in terms of its ability to distinguish between characteristically different data distributions among different classes of objects.


Data Stream Outlier Detection Elliptical Galaxy Fundamental Plane Distance Distribution Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is supported in part by NASA AISR grant number NNX07AV70G and in part by NASA through the American Astronomical Society’s Small Research Grant Program.


  1. 1.
    M. J. Bayarri and J. O. Berger. Measures of Surprise in Bayesian Analysis. Downloaded from, 1997.
  2. 2.
    M. J. Bayarri and J. O. Berger. Quantifying Surprise in the Data and Model Verification. Downloaded from, 1998.
  3. 3.
    B. Berriman, D. Kirkpatrick, R. Hanisch, A. Szalay, and R. Williams. Discovery of Brown Dwarfs with Virtual Observatories. IAU Joint Discussion 8: Large Telescopes and Virtual Observatory: Visions for the Future.
  4. 4.
    K. Borne. Scientific Data Mining in Astronomy. Next Generation Data Mining. CRC Press: Taylor & Francis, Boca Raton, FL, pp. 91–114, 2009.Google Scholar
  5. 5.
    K. Borne. Effective Outlier Detection using K-Nearest Neighbor Data Distributions: Unsupervised Exploratory Mining of Non-Stationarity in Data Streams. Submitted to the Machine Learning Journal, March 2010.Google Scholar
  6. 6.
    M. Breunig, H.-P. Kriegel, R. Ng, and S. Sander. LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record, vol. 29, pp. 93–104, 2000.CrossRefGoogle Scholar
  7. 7.
    K. Das, K. Bhaduri, S. Arora, W. Griffin, K. Borne, C. Giannella, and H. Kargupta. Scalable Distributed Change Detection from Astronomy Data Streams using Eigen-Monitoring Algorithms. 2009 SIAM International Conference on Data Mining (SDM09), 2009.Google Scholar
  8. 8.
    D. L. Davies and D. W. Bouldin. A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2): 224–227, 1979.CrossRefGoogle Scholar
  9. 9.
    M. Debruyne. An Outlier Map for Support Vector Machine Classification. Annals of Applied Statistics, 3(4): 1566–1580, 2009.MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    P. Dhaliwal, M. Bhatia, and P. Bansal, P. A Cluster-based Approach for Outlier Detection in Dynamic Data Streams (KORM: K-median OutlieR Miner). Journal of Computing, vol. 2, pp. 74–80, 2010.Google Scholar
  11. 11.
    S. G. Djorgovski and M. Davis. Fundamental Properties of Elliptical Galaxies. Astrophysical Journal, vol. 313, pp. 59–68, 1987.CrossRefGoogle Scholar
  12. 12.
    A. Dressler, D. Lynden-Bell, D. Burstein, R. L. Davies, S. M. Faber, R. Terlevich, and G. Wegner. Spectroscopy and Photometry of Elliptical Galaxies. I - A New Distance Estimator. Astrophysical Journal, vol. 313, pp. 42–58, 1987.Google Scholar
  13. 13.
    H. Dutta. Empowering Scientific Discovery by Distributed Data Mining on the Grid Infrastructure. Ph.D. dissertation, UMBC, 2007.Google Scholar
  14. 14.
    H. Dutta, C. Giannella, K. Borne, and H. Kargupta. Distributed Top-K Outlier Detection from Astronomy Catalogs using the DEMAC System. 2007 SIAM International Conference on Data Mining, 2007.Google Scholar
  15. 15.
    H. Dutta, C. Giannella, K. Borne, H. Kargupta, and R. Wolff. Distributed Data Mining for Astronomy Catalogs. IEEE Transactions in Knowledge and Data Engineering, 2009.Google Scholar
  16. 16.
    P. Filzmoser, R. Maronna, and M. Werner. Outlier Identification in High Dimensions. Computational Statistics and Data Analysis, 52, pp. 1694–1711, 2008.MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    A. Freitas On Objective Measures of Rule Surprisingness. LNCC, 1510, pp. 1–9, 1998.Google Scholar
  18. 18.
    V. Hautamaki, I. Karkkainen, and P. Franti. Outlier Detection Using k-Nearest Neighbour Graph. Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), 2004.Google Scholar
  19. 19.
    C. R. Johnson, M. Glatter, W. Kendall, J. Huang, and F. Hoffman. Querying for Feature Extraction and Visualization in Climate Modeling. ICCS 2009, Part II, LNCS 5545, pp. 416–425, 2009.Google Scholar
  20. 20.
    G. I. G. Jozsa, M. A. Garrett, T. A. Oosterloo, H. Rampadarath, Z. Paragi, H. van Arkel, C. Lintott, W. C.Keel, K. Schawinski, and E. Edmondson. Revealing Hanny’s Voorwerp: Radio Observations of IC 2497. Astronomy and Astrophysics, vol. 500, pp. L33–L36, 2009.CrossRefGoogle Scholar
  21. 21.
    C. J. Lintott, et al. Galaxy Zoo: Morphologies Derived from Visual Inspection of Galaxies from the Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society, vol. 389, pp. 1179–1189, 2008.CrossRefGoogle Scholar
  22. 22.
    R. A. Maronna and V. J. Yohai. The Behavior of the Stahel-Donoho Robust Multivariate Estimator. Journal of the American Statistical Association, vol. 90, pp. 330–341, 1995.MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    D. Pena and F. J. Prieto. Multivariate Outlier Detection and Robust Covariance Matrix Estimation. Technometrics, vol. 43, pp. 286–301, 2001.MathSciNetCrossRefGoogle Scholar
  24. 24.
    D. Pokrajac, A. Lazarevic, and L. Latecki, L. Incremental Local Outlier Detection for Data Streams. IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2007.Google Scholar
  25. 25.
    G. T. Richards, et al. Eight-Dimensional Mid-Infrared/Optical Bayesian Quasar Selection. Astronomical Journal, vol. 137, pp. 3884–3899, 2009.CrossRefGoogle Scholar
  26. 26.
    V. Saltenis. Outlier Detection Based on the Distribution of Distances between Data Points. Informatica, 15(3): 399–410, 2004.MathSciNetGoogle Scholar
  27. 27.
    R.-D. Scholz, M. J. McCaughrean, N. Lodieu, and B. Kuhlbrodt. Epsilon Indi B: A New Benchmark T Dwarf. Astronomy and Astrophysics, vol. 398, pp. L29–L33, 2003.CrossRefGoogle Scholar
  28. 28.
    A. A. Shabalin, V. J. Weigman, C. M. Perou, and A. B. Nobel. Finding Large Average Submatrices in High Dimensional Data. Annals of Applied Statistics, 3(3): 985–1012, 2009.MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    S. S. Shapiro and M. B. Wilk. An analysis of variance test for normality (complete samples). Biometrika, vol. 52, pp. 591–611, 1965.MathSciNetzbMATHGoogle Scholar
  30. 30.
    P. Smyth and R. M. Goodman. Rule Induction Using Information Theory. Knowledge Discovery in Databases, pp 159–176, AAAI/MIT Press, 1991.Google Scholar
  31. 31.
    S. Srinoy and W. Kurutach. Anomaly Detection Model Based on Bio-Inspired Algorithm and Independent Component Analysis. TENCON 2006, IEEE Region 10 Conference proceedings, pp. 1–4, 2006.Google Scholar
  32. 32.
    Weaver’s Surprise Index. Encyclopedia of Statistical Sciences (Wiley), vol. 9, pp. 104–109, 1988.Google Scholar
  33. 33.
    N. Zakamska, et al. Candidate Type II Quasars from the Sloan Digital Sky Survey. I. Selection and Optical Properties. Astronomical Journal, vol. 126, pp. 2125–2143, 2003.Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Astronomy, & Computational Science, School of PhysicsGeorge Mason UniversityFairfaxUSA

Personalised recommendations