Discovering most classificatory patterns for very expressive pattern classes

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

研究成果: Contribution to journalArticle査読

14 被引用数 (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.

ジャーナルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版ステータス出版済み - 1 1 2003

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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