A survey of longest common subsequence algorithms

By L. Bergroth, H. Hakonen, T. Raita. 2000

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From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “…a sequence of discrete variables subject to a monotonicity constraint can be solved efﬁciently using dynamic programming: it is related to the Longest Common Subsequence problem [4], which admits a solution whose running time (per user) is bilinear in E and the number of ratings in their history.”
Essential Body-Joint and Atomic Action Detection for Human Activity Recognition Using Longest Common Subsequence Algorithm (2012, methodology): “Next, we will present our main algorithm on how to learn essential body-joint and atomic action from training data using the Longest Common Subsequence (LCS) algorithm [23, 24].”
What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures (2016, background): “The length of the longest common subsequence (LCS) corresponds to the number of elements in one sequence that can be uniquely matched with elements occurring in the same order in the other sequence (for example see Bergroth et al. (2000)).”
Time-series clustering - A decade review (2015, methodology): “Shape averaging using Longest Common Sub-Sequence (LCSS): the longest common subsequence [159] generally permits to make a summary of a set of sequences.”
Data Matching (2012, unknown): “A generalisation of the longest substring algorithm is to consider longest common sub-sequences as well [27].”