Abstract
Most modern lossless data compression techniques used today, are based in dictionaries. If some string of data being compressed matches a portion previously seen, then such string is included in the dictionary and its reference is included every time it appears. A possible generalization of this scheme is to consider not only strings made of consecutive symbols, but more general patterns with gaps between its symbols. The main problems with this approach are the complexity of pattern discovery algorithms and the complexity for the selection of a good subset of patterns. In this paper we address the last of these problems. We demonstrate that such problem is NP-complete and we provide some preliminary results about heuristics that points to its solution.
Categories and Subject Descriptors: E.4 [Coding and Information Theory]–data compaction and compression; F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Problems; I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search–heuristic methods.
General Terms: Algorithms, Theory
Additional Key Words and Phrases: Genetic algorithms, optimization, NP-hardness
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Kuri, Á., Galaviz, J. (2004). Pattern-Based Data Compression. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_1
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DOI: https://doi.org/10.1007/978-3-540-24694-7_1
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