Algorithms for approximate string matching
Information and Control
A linear space algorithm for the LCS problem
Acta Informatica
Introduction to algorithms
An O(NP) sequence comparison algorithm
Information Processing Letters
A fast algorithm for computing longest common subsequences of small alphabet size
Journal of Information Processing
Fast linear-space computations of longest common subsequences
Theoretical Computer Science - Selected papers of the Combinatorial Pattern Matching School
Sparse dynamic programming I: linear cost functions
Journal of the ACM (JACM)
The String-to-String Correction Problem
Journal of the ACM (JACM)
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
A fast algorithm for computing longest common subsequences
Communications of the ACM
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
MFCS '94 Proceedings of the 19th International Symposium on Mathematical Foundations of Computer Science 1994
The Longest Common Subsequence Problem for Small Alphabet Size Between Many Strings
ISAAC '92 Proceedings of the Third International Symposium on Algorithms and Computation
Two Algorithms for the Longest Common Subsequence of Three (or More) Strings
CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
New Algorithms for the Longest Common Subsequence Problem
New Algorithms for the Longest Common Subsequence Problem
Efficient Computation of All Longest Common Subsequences
SWAT '00 Proceedings of the 7th Scandinavian Workshop on Algorithm Theory
Surfing Notes: An Integrated Web Annotation and Archiving Tool
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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Given two sequences A = a1a2... am and B = b1b2... bn, m ≤ n, over some alphabet Σ of size s, the longest common subsequence problem is to find a sequence of greatest possible length, p, that can be obtained from both A and B by deleting zero or more (not necessarily adjacent) symbols. A new algorithm that is efficient for both short and long longest common subsequences is presented. It also improves on previous methods for longest common subsequences of intermediate length. Thus, it is more flexible and can be used for a wider range of applications than others. The algorithm is based on the well-known paradigm of computing dominant matches and was obtained by observing additional structural properties leading to a kind of dualization. The worst-case running time of the algorithm is O(ns+min{pm, p(n-p)}). Some experimental results which prove the practicability of the new method are given, too.