Pattern Mining in Ultra-High Frequency Order Books with Self-Organizing Maps

  • Piotr LipinskiEmail author
  • Anthony Brabazon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)


This paper addresses the issue of discovering frequent patterns in order book shapes, in the context of the stock market depth, for ultra-high frequency data. It proposes a computational intelligence approach to building frequent patterns by clustering order book shapes with Self-Organizing Maps. An experimental evaluation of the approach proposed on the London Stock Exchange Rebuild Order Book database succeeded with providing a number of characteristic shape patterns and also with estimating probabilities of some typical transitions between shape patterns in the order book.


Frequent Pattern Pattern Mining Transition Probability Matrix Order Book Neighborhood Relation 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Computational Intelligence Research Group, Institute of Computer ScienceUniversity of WroclawWroclawPoland
  2. 2.Natural Computing Research and Applications Group, Complex and Adaptive Systems LaboratoryUniversity College DublinDublinIreland

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