Fast q-gram mining on SLP compressed strings

  • Authors:

  • Keisuke Goto;Hideo Bannai;Shunsuke Inenaga;Masayuki Takeda

  • Affiliations:

  • Department of Informatics, Kyushu University, Japan;Department of Informatics, Kyushu University, Japan;Department of Informatics, Kyushu University, Japan;Department of Informatics, Kyushu University, Japan

  • Venue:
  • Journal of Discrete Algorithms
  • Year:
  • 2013

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Abstract

We present simple and efficient algorithms for calculating q-gram frequencies on strings represented in compressed form, namely, as a straight line program (SLP). Given an SLP of size n that represents string T, we present an O(qn) time and space algorithm that computes the occurrence frequencies of all q-grams in T. Computational experiments show that our algorithm and its variation are practical for small q, actually running faster on various real string data, compared to algorithms that work on the uncompressed text. We also discuss applications in data mining and classification of string data, for which our algorithms can be useful.