Using Maximum Subarrays for Approximate String Matching

Ramazan S. Aygun ()
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Ramazan S. Aygun: University of Alabama in Huntsville

Annals of Data Science, 2017, vol. 4, issue 4, No 5, 503-531

Abstract: Abstract In this paper, we evaluate maximum subarrays for approximate string matching and alignment. The global alignment score as well as local sub-alignments are indicators of good alignment. After showing how maximum sub-arrays could be used for string matching, we provide several ways of using maximum subarrays: long, short, loose, strict, and top-k. While long version extends the local sub-alignments, the short method avoids extensions that would not increase the alignment score. The loose method tries to achieve high global score whereas the strict method converts the output of loose alignment by minimizing the unnecessary gaps. The top-k method is used to find out top-k sub-alignments. The results are compared with two global and local dynamic programming methods that use gap penalties in addition to one of the state-of-art methods. In our experiments, using maximum subarrays generated good overall as well as local sub-alignments without requiring gap penalties.

Keywords: String matching; Sequence alignment; Maximum subarrays (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s40745-017-0117-0

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