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Industrial Conference on Data Mining

ICDM 2008: Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects pp 374–388Cite as

Efficient String Mining under Constraints Via the Deferred Frequency Index

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5077))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Free University of Berlin, Takustr. 9, 14195, Berlin, Germany

    David Weese

  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,  

    Marcel H. Schulz

Authors
  1. David Weese
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  2. Marcel H. Schulz
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Editor information

Petra Perner

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Weese, D., Schulz, M.H. (2008). Efficient String Mining under Constraints Via the Deferred Frequency Index. In: Perner, P. (eds) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. ICDM 2008. Lecture Notes in Computer Science(), vol 5077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70720-2_29

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  • DOI: https://doi.org/10.1007/978-3-540-70720-2_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70717-2

  • Online ISBN: 978-3-540-70720-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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