One-dimensional approximate point set pattern matching with Lp-norm

  • Authors:
  • Hung-Lung Wang;Kuan-Yu Chen

  • Affiliations:
  • Institute of Information and Decision Sciences, National Taipei College of Business, No. 321, Sec. 1, Jinan Road, Taipei 100, Taiwan, ROC;Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, ROC

  • Venue:
  • Theoretical Computer Science
  • Year:
  • 2014

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Abstract

Given two sets of points, the text and the pattern, determining whether the pattern ''appears'' in the text is modeled as the point set pattern matching problem. Applications usually ask for not only exact matches between these two sets, but also approximate matches. In this paper, we investigate a one-dimensional approximate point set pattern matching problem proposed in [19]. We generalize the measure between approximate matches from L"1-norm to L"p-norm. More specifically, what requested is an optimal match which minimizes the L"p-norm of the difference vector (|p"2-p"1-(t"2^'-t"1^')|,|p"3-p"2-(t"3^'-t"2^')|,...,|p"m-p"m"-"1-(t"m^'-t"m"-"1^')|), where p"1,p"2,...,p"m is the pattern and t"1^',t"2^',...,t"m^' is a subsequence of the text. For p-~, we propose an O(mn)-time algorithm, where m and n denote the lengths of the pattern and the text, respectively. For arbitrary p