Interesting Multi-Relational Patterns
Eirini Spyropoulou and Tijl De Bie
In: ICDM 2011, 11 - 14 Dec 2011, Vancouver, Canada.
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 highly simplified types of data, such as an
attribute-value table or a binary database, such that those
methods 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
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 subgraphs
in a representation of the database as a K-partite graph. We
show how this pattern syntax is generally applicable to multirelational data, while it reduces to well-known tiles when
the data is a simple binary or attribute-value table. We propose RMiner, an efficient algorithm to mine such patterns, and we introduce a method for quantifying their interestingness when contrasted with prior information of the data miner. Finally, we illustrate the usefulness of our approach by discussing results on real-world and synthetic databases.