Discovering most classificatory patterns for very expressive pattern classes

Masayuki Takeda, Shunsuke Inenaga, Hideo Bannai, Ayumi Shinohara, Setsuo Arikawa

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


The classificatory power of a pattern is measured by how well it separates two given sets of strings. This paper gives practical algorithms to find the fixed/variable-length-don't-care pattern (FVLDC pattern) and approximate FVLDC pattern which are most classificatory for two given string sets. We also present algorithms to discover the best window-accumulated FVLDC pattern and window-accumulated approximate FVLDC pattern. All of our new algorithms run in practical amount of time by means of suitable pruning heuristics and fast pattern matching techniques.

Original languageEnglish
Pages (from-to)486-493
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2003

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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