Optimal String Mining Under Frequency Constraints

  • Johannes Fischer
  • Volker Heun
  • Stefan Kramer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


We propose a new algorithmic framework that solves frequency-related data mining queries on databases of strings in optimal time, i.e., in time linear in the input and the output size. The additional space is linear in the input size. Our framework can be used to mine frequent strings, emerging strings and strings that pass other statistical tests, e.g., the χ 2-test. In contrast to the presented result for strings, no optimal algorithms are known for other pattern domains such as itemsets. The key to our approach are several recent results on index structures for strings, among them suffix- and lcp-arrays, and a new preprocessing scheme for range minimum queries. The advantages of array-based data structures (compared with dynamic data structures such as trees) are good locality behavior and extensibility to secondary memory. We test our algorithm on real-world data from computational biology and demonstrate that the approach also works well in practice.


Index Structure Lexicographic Order Secondary Memory Pattern Domain Frequency Constraint 
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 2006

Authors and Affiliations

  • Johannes Fischer
    • 1
  • Volker Heun
    • 1
  • Stefan Kramer
    • 2
  1. 1.Institut für InformatikLudwig-Maximilians-Universität MünchenMünchen
  2. 2.Institut für Informatik/I12Technische Universität MünchenGarching b. München

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