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

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	486-493
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2843
DOIs	https://doi.org/10.1007/978-3-540-39644-4_50
Publication status	Published - 2003
Externally published	Yes

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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

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Discovering most classificatory patterns for very expressive pattern classes. / Takeda, Masayuki; Inenaga, Shunsuke; Bannai, Hideo et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2843, 2003, p. 486-493.

Research output: Contribution to journal › Article › peer-review

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AU - Arikawa, Setsuo

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AB - 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.

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Discovering most classificatory patterns for very expressive pattern classes

Abstract

ASJC Scopus subject areas

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