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Discovering Frequent Substructures in Large Unordered Trees

  • Tatsuya Asai
  • Hiroki Arimura
  • Takeaki Uno
  • Shin-ichi Nakano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

In this paper, we study a frequent substructure discovery problem in semi-structured data. We present an efficient algorithm Unotthat computes all frequent labeled unordered trees appearing in a large collection of data trees with frequency above a user-specified threshold. The keys of the algorithm are efficient enumeration of all unordered trees in canonical form and incremental computation of their occurrences. We then show that Unotdiscovers each frequent pattern T in O(kb 2 m) per pattern, where k is the size of T, b is the branching factor of the data trees, and m is the total number of occurrences of T in the data trees.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tatsuya Asai
    • 1
  • Hiroki Arimura
    • 1
  • Takeaki Uno
    • 2
  • Shin-ichi Nakano
    • 3
  1. 1.Kyushu UniversityFukuokaJAPAN
  2. 2.National Institute of InformaticsTokyoJAPAN
  3. 3.Gunma UniversityKiryu-shi, GunmaJAPAN

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