Abstract

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