Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential\udoccurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there\udexists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences.\udThis article begins with the introduction of a model for the representation of sequential patterns—Sequential\udPatterns Graph—which motivates the search for new structural relation patterns. An integrative framework for\udthe discovery of these patterns–Postsequential Patterns Mining–is then described which underpins the postprocessing\udof sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing\udis proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three\udcomponent algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides\udan efficient method for structural knowledge discovery
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