Simple Pattern Spectrum Estimation for Fast Pattern Filtering with CoCoNAD

  • Christian Borgelt
  • David Picado-Muiño
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8819)


CoCoNAD (for Continuous-time Closed Neuron Assembly Detection) is an algorithm for finding frequent parallel episodes in event sequences, which was developed particularly for neural spike train analysis. It has been enhanced by so-called Pattern Spectrum Filtering (PSF), which generates and analyzes surrogate data sets to identify statistically significant patterns, and Pattern Set Reduction (PSR), which eliminates spurious induced patterns. A certain drawback of the former is that a sizable number of surrogates (usually several thousand) have to be generated and analyzed in order to achieve reliable results, which can render the analysis process slow (depending on the analysis parameters). However, since the structure of a pattern spectrum is actually fairly simple, we propose a simple estimation method, with which (an approximation of) a pattern spectrum can be derived from the original data, bypassing the time-consuming generation and analysis of surrogate data sets.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abdi, H., Bonferroni, Šidák: Corrections for Multiple Comparisons. In: Salkind, N.J. (ed.) Encyclopedia of Measurement and Statistics, pp. 103–107. Sage Publications, Thousand Oaks (2007)Google Scholar
  2. 2.
    Bonferroni, C.E.: Il calcolo delle assicurazioni su gruppi di teste. Studi in Onore del Professore Salvatore Ortu Carboni, pp. 13–60. Bardi, Rome (1935)Google Scholar
  3. 3.
    Borgelt, C.: Frequent Item Set Mining. Wiley Interdisciplinary Reviews (WIREs): Data Mining and Knowledge Discovery 2, 437–456 (2012)Google Scholar
  4. 4.
    Borgelt, C., Picado-Muiño, D.: Finding Frequent Synchronous Events in Parallel Point Processes. In: Proc. 12th Int. Symposium on Intelligent Data Analysis (IDA 2013), pp. 116–126. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Fiedler, M., Borgelt, C.: Subgraph Support in a Single Graph. In: Proc. IEEE Int. Workshop on Mining Graphs and Complex Data, pp. 399–404. IEEE Press, Piscataway (2007)Google Scholar
  6. 6.
    Gwadera, R., Atallah, M., Szpankowski, W.: Markov Models for Identification of Significant Episodes. In: Proc. 2005 SIAM Int. Conf. on Data Mining, pp. 404–414. Society for Industrial and Applied Mathematics, Philadelphia (2005)CrossRefGoogle Scholar
  7. 7.
    Høastad, J.: Clique is Hard to Approximate within n 1e. Acta Mathematica 182, 105–142 (1999)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hebb, D.: The Organization of Behavior. J. Wiley & Sons, New York (1949)Google Scholar
  9. 9.
    Karp, R.M.: Reducibility among Combinatorial Problems. In: Miller, R.E., Thatcher, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press, New York (1972)CrossRefGoogle Scholar
  10. 10.
    Laxman, S., Sastry, P.S., Unnikrishnan, K.: Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection. IEEE Trans. on Knowledge and Data Engineering 17(11), 1505–1517 (2005)CrossRefGoogle Scholar
  11. 11.
    Louis, S., Borgelt, C., Grün, S.: Generation and Selection of Surrogate Methods for Correlation Analysis. In: Grün, S., Rotter, S. (eds.) Analysis of Parallel Spike Trains, pp. 359–382. Springer, Berlin (2010)CrossRefGoogle Scholar
  12. 12.
    Mannila, H., Toivonen, H., Verkamo, A.: Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)CrossRefGoogle Scholar
  13. 13.
    Picado-Muiño, D., Borgelt, C., Berger, D., Gerstein, G.L., Grün, S.: Finding Neural Assemblies with Frequent Item Set Mining. Frontiers in Neuroinformatics, 7: article 9 (2013) doi:10.3389/fninf.2013.00009Google Scholar
  14. 14.
    Picado-Muiño, D., Borgelt, C.: Frequent Itemset Mining for Sequential Data: Synchrony in Neuronal Spike Trains. In: Intelligent Data Analysis. IOS Press, Amsterdam (to appear, 2014)Google Scholar
  15. 15.
    Tatti, N.: Significance of Episodes Based on Minimal Windows. In: Proc. 9th IEEE Int. Conf. on Data Mining (ICDM 2009), pp. 513–522. IEEE Press, Piscataway (2009)CrossRefGoogle Scholar
  16. 16.
    Torre, E., Picado-Muiño, D., Denker, M., Borgelt, C., Grün, S.: Statistical Evaluation of Synchronous Spike Patterns Extracted by Frequent tem Set Mining. Frontiers in Computational Neuroscience 7, article 132 (2013), doi:10.3389/fninf.2013.00132Google Scholar
  17. 17.
    Vanetik, N., Gudes, E., Shimony, S.E.: Computing Frequent Graph Patterns from Semistructured Data. In: Proc. IEEE Int. Conf. on Data Mining, pp. 458–465. IEEE Press, Piscataway (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Christian Borgelt
    • 1
  • David Picado-Muiño
    • 1
  1. 1.European Centre for Soft ComputingMieresSpain

Personalised recommendations