Efficient String Mining under Constraints Via the Deferred Frequency Index

  • David Weese
  • Marcel H. Schulz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5077)


We propose a general approach for frequency based string mining, which has many applications, e.g. in contrast data mining. Our contribution is a novel algorithm based on a deferred data structure. Despite its simplicity, our approach is up to 4 times faster and uses about half the memory compared to the best-known algorithm of Fischer et al. Applications in various string domains, e.g. natural language, DNA or protein sequences, demonstrate the improvement of our algorithm.


Frequency Vector Frequent Pattern Mining Space Consumption Union String Frequency Predicate 
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 2008

Authors and Affiliations

  • David Weese
    • 1
  • Marcel H. Schulz
    • 2
  1. 1.Department of Computer ScienceFree University of BerlinBerlinGermany
  2. 2.Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestr. 73, 14195 Berlin, Germany and, International Max Planck Research School for Computational Biology and Scientific Computing 

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