MIST: Top-k Approximate Sub-string Mining Using Triplet Statistical Significance

  • Sourav Dutta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)


Efficient extraction of strings or sub-strings similar to an input query string forms a necessity in applications like instant search, record linkage, etc., where the similarity between two strings is usually quantified by edit distance. This paper proposes a novel top-k approximate sub-string matching algorithm, MIST, for a given query, based on Chi-squared statistical significance of string triplets, thereby avoiding expensive edit distance computation. Experiments with real-life data validate the run-time effectiveness and accuracy of our algorithm.


Approx. string search Edit distance χ2 statistical significance n-grams 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sourav Dutta
    • 1
  1. 1.Max-Planck Institute for InformaticsGermany

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