A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database
This is an incremental improvement for data mining researchers, offering a unified framework to handle diverse constraints in sequential pattern mining.
The paper tackled the problem of mining sequential patterns with multiple constraints by proposing a Constraint Programming (CP) approach, showing feasibility and interest in experiments.
Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In the context of sequential pattern mining, a large number of devoted techniques have been developed for solving particular classes of constraints. The aim of this paper is to investigate the use of Constraint Programming (CP) to model and mine sequential patterns in a sequence database. Our CP approach offers a natural way to simultaneously combine in a same framework a large set of constraints coming from various origins. Experiments show the feasibility and the interest of our approach.