Discovering most classificatory patterns for very expressive pattern classes

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

doi:10.1007/978-3-540-39644-4_50

Discovering most classificatory patterns for very expressive pattern classes

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

数理情報

研究成果: Contribution to journal › Article › 査読

14 被引用数 (Scopus)

抄録

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.

本文言語	英語
ページ（範囲）	486-493
ページ数	8
ジャーナル	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
巻	2843
DOI	https://doi.org/10.1007/978-3-540-39644-4_50
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)

文献へのアクセス

10.1007/978-3-540-39644-4_50

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引用スタイル

Discovering most classificatory patterns for very expressive pattern classes. / Takeda, Masayuki ; Inenaga, Shunsuke; Bannai, Hideo; Shinohara, Ayumi; Arikawa, Setsuo.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2843, 01.01.2003, p. 486-493.

研究成果: Contribution to journal › Article › 査読

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