Sequential pattern mining is one of the important functionalities of data mining. It is used for analyzing sequential database and discovers sequential patterns. It is focused for extracting interesting subsequences from a set of sequences. Various factors such as rate of occurrence, length, and profit are used to define the interestingness of subsequence derived from the sequence database. Sequential pattern mining has abundant real-life applications since sequential data is logically programmed as sequences of cipher in many fields such as bioinformatics, e-learning, market basket analysis, texts, and webpage click-stream analysis. A large diversity of competent algorithms such as Prefixspan, GSP and Freespan have been proposed during the past few years. In this paper we propose a data model for organizing the sequential database, which consists of a directed graph DGS (cycles and several edges are allowed) and an organization of directed paths in DGS to represent a sequential data for discovering sequential pattern3 from a sequence database. Competent algorithms for constructing the digraph model (DGS) for extracting all sequential patterns and mining association rules are proposed. A number of theoretical parameters of digraph model are also introduced, which lead to more understanding of the problem.
A Neural Multi-digraph Model for Chinese NER with Gazetteers