Constraint-Based Sequential Pattern Mining with Decision Diagrams

  • Amin Hosseininasab Carnegie Mellon University
  • Willem-Jan van Hoeve Carnegie Mellon University
  • Andre A. Cire University of Toronto Scarborough


Constraint-based 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 (MDD) 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.

AAAI Technical Track: Constraint Satisfaction and Optimization