Abstract
We consider the well known problem of pattern matching under the Hamming distance. Previous approaches have shown how to count the number of mismatches efficiently, especially when a bound is known for the maximum Hamming distance. Our interest is different in that we wish collect a random sample of mismatches of fixed size at each position in the text. Given a pattern p of length m and a text t of length n, we show how to sample with high probability c mismatches where possible from every alignment of p and t in O((c + logn)(n + mlogm)logm) time. Further, we guarantee that the mismatches are sampled uniformly and can therefore be seen as representative of the types of mismatches that occur.
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Clifford, R., Efremenko, K., Porat, B., Porat, E., Rothschild, A. (2008). Mismatch Sampling. In: Amir, A., Turpin, A., Moffat, A. (eds) String Processing and Information Retrieval. SPIRE 2008. Lecture Notes in Computer Science, vol 5280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89097-3_11
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DOI: https://doi.org/10.1007/978-3-540-89097-3_11
Publisher Name: Springer, Berlin, Heidelberg
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