A change detection method for sequential patterns

Recent trends in customer-oriented markets drive many researchers to develop sequential pattern mining algorithms to explore consumer behaviors. However, most of these studies concentrated on how to improve accuracy and efficiency of their methods, and seldom discussed how to detect sequential pattern changes between two time-periods. To help business managers understand the changing behaviors of their customers, a three-phase sequential pattern change detection framework is proposed in this paper. In phase I, two sequential pattern sets are generated respectively from two time-period databases. In phase II, the dissimilarities between all pairs of sequential patterns are evaluated using the proposed sequential pattern matching algorithm. Based on a set of judgment criteria, a sequential pattern is clarified as one of the following three change types: an emerging sequential pattern, an unexpected sequence change, or an added/perished sequential pattern. In phase III, significant change patterns are returned to managers if the degree of change for a pattern is large enough. A practical transaction database is demonstrated to show how the proposed framework helps managers to analyze their customers and make better marketing strategies.