Mining Interesting Patterns in Multi-Relational Data
Eirini Spyropoulou, Tijl De Bie, Mario Boley · Bristol Research (University of Bristol) · 2013
Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community.Traditional data mining approaches are typically developed for single-table databases, and are not directly applicable to multi-relational data.Nevertheless, multi-relational data is a more truthful and therefore often also a more powerful representation of reality.Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance.In this paper we introduce a novel approach to mining patterns in multi-relational data.We propose a new syntax for multi-relational patterns as complete connected subsets of database entities.We show how this pattern syntax is generally applicable to multi-relational data, while it reduces to well-known tiles (Geerts et al 2004) when the data is a simple binary or attribute-value table.We propose RMiner, a simple yet practically efficient divide and conquer algorithm to mine such patterns which is an instantiation of an algorithmic framework for efficiently enumerating all fixed points of a suitable closure operator (Boley et al 2010).We show how the interestingness of patterns of the proposed syntax can conveniently be quantified using a general