Seq2Pat: Sequence-to-Pattern Generation for Constraint-Based Sequential Pattern Mining
Xin Wang, Amin Hosseininasab, Pablo Colunga, Serdar Kadıoğlu, Willem‐Jan van Hoeve
Abstract
Pattern mining is an essential part of knowledge discovery and data analytics. It is a powerful paradigm, especially when combined with constraint reasoning. In this paper, we present Seq2Pat, a constraint-based sequential pattern mining tool with a high-level declarative user interface. The library finds patterns that frequently occur in large sequence databases subject to constraints. We highlight key benefits that are desirable, especially in industrial settings where scalability, explainability, rapid experimentation, reusability, and reproducibility are of great interest. We then showcase an automated feature extraction process powered by Seq2Pat to discover high-level insights and boost downstream machine learning models for customer intent prediction.