A taxonomy of sequential pattern mining algorithms

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A Survey of Sequential Pattern Mining (2017, background): “It is a prominent solution for analyzing sequential data [2, 98, 117, 4, 51, 89, 3, 47, 30, 111, 31, 32, 27, 28, 22, 100, 79].”
Prediction of Human Activity by Discovering Temporal Sequence Patterns (2014, background): “Sequential pattern mining [33], [55] was first introduced in the area of data mining, where a sequence database stores a set of records consisting of sequences of ordered events, with or without concrete notions of time.”
USpan: an efficient algorithm for mining high utility sequential patterns (2012, methodology): “Frequent sequential pattern mining is a very popular topic [1, 9], with quite a few algorithms, such as SPADE [14], Prefixspan [10] and SPAM [5], proposed on the support/frequency framework.”
Sequence-Aware Recommender Systems (2018, methodology): “ng ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: February 2018. 1:4 M. Quadrana et al. patterns do not have to be made explicit, as often done, e.g., in sequential pattern mining [79], but can be implicitly encoded in complex machine learning models as well. Besides the identifi”