A Survey of Sequential Pattern Mining

Discovering unexpected and useful patterns in databases is a fundamental data mining task. In recent years, a trend in data mining has been to design algorithms for discovering patterns in sequential data. One of the most popular data mining tasks on sequences is sequential pattern mining. It consists of discovering interesting subsequences in a set of sequences, where the interestingness of a subsequence can be measured in terms of various criteria such as its occurrence frequency, length, and profit. Sequential pattern mining has many real-life applications since data is encoded as sequences in many fields such as bioinformatics, e-learning, market basket analysis, text analysis, and webpage click-stream analysis. This paper surveys recent studies on sequential pattern mining and its applications. The goal is to provide both an introduction to sequential pattern mining, and a survey of recent advances and research opportunities. The paper is divided into four main parts. First, the task of sequential pattern mining is defined and its applications are reviewed. Key concepts and terminology are introduced. Moreover, main approaches and strategies to solve sequential pattern mining problems are presented. Limitations of traditional sequential pattern mining approaches are also highlighted, and popular variations of the task of sequential pattern mining are presented. The paper also presents research opportunities and the relationship to other popular pattern mining problems. Lastly, the paper also discusses open-source implementations of sequential pattern mining algorithms.

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