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. © Springer-Verlag Berlin Heidelberg 2003.
Cite
CITATION STYLE
Takeda, M., Inenaga, S., Bannai, H., Shinohara, A., & Arikawa, S. (2003). Discovering most classificatory patterns for very expressive pattern classes. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2843, 486–493. https://doi.org/10.1007/978-3-540-39644-4_50
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