Constraint-Based Pattern Mining in Multi-relational Databases

Siegfried Nijssen, Aída Jiménez, Tias Guns

Abstract: We propose a new framework for constraint-based pattern mining in multi-relational databases. Distinguishing features of the framework are that (1) it allows finding patterns not only under anti-monotonic constraints, but also under monotonic constraints and closed ness constraints, among others, expressed over complex aggregates over multiple relations, (2) it builds on a declarative graphical representation of constraints that links closely to data models of multi-relational databases and constraint networks in constraint programming, (3) it maps multi-relational pattern mining tasks into constraint programs. Our framework builds on a unifying perspective of multi-relational pattern mining, relational database technology and constraint networks in constraint programming. We demonstrate our framework on IMDB and Finance multi-relational databases.