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.




How to Cite

Hosseininasab, A., Hoeve, W.-J. van, & Cire, A. A. (2019). Constraint-Based Sequential Pattern Mining with Decision Diagrams. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1495-1502.



AAAI Technical Track: Constraint Satisfaction and Optimization