Abstract
The problem of computing similarity of two run-length encoded strings has been studied for various scoring metrics. Many algorithms have been developed for the longest common subsequence metric and some algorithms for the Levenshtein distance metric and the weighted edit distance metric. In this paper we consider similarity based on the affine gap penalty metric which is a more general and rather complicated scoring metric than the weighted edit distance. To compute similarity in this model efficiently, we convert the problem to a path problem on a directed acyclic graph and use some properties of maximum paths in this graph. We present an O(nm′+n′m) time algorithm for computing similarity of two run-length encoded strings in the affine gap penalty model, where n′ and m′ are the lengths of given two run-length encoded strings, and n and m are the decoded lengths of given two strings, respectively.
This work was supported by FPR05A2-341 of 21C Frontier Functional Proteomics Project from Korean Ministry of Science & Technology.
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Kim, J.W., Amir, A., Landau, G.M., Park, K. (2005). Computing Similarity of Run-Length Encoded Strings with Affine Gap Penalty. In: Consens, M., Navarro, G. (eds) String Processing and Information Retrieval. SPIRE 2005. Lecture Notes in Computer Science, vol 3772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11575832_35
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DOI: https://doi.org/10.1007/11575832_35
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