Constraint-based Sequential Pattern Mining with Decision Diagrams
This work addresses the challenge of efficiently mining sequential patterns with complex constraints, which is important for data mining applications in domains like retail or web analytics.
The authors tackled the problem of constrained sequential pattern mining by introducing a multi-valued decision diagram representation that accommodates multiple item attributes and non-monotone constraints. Their MDD-based prefix-projection algorithm showed competitive or superior scalability and efficiency compared to existing methods.
Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.