Data Mining and Knowledge Discovery

, Volume 28, Issue 3, pp 808–849

Interesting pattern mining in multi-relational data


DOI: 10.1007/s10618-013-0319-9

Cite this article as:
Spyropoulou, E., De Bie, T. & Boley, M. Data Min Knowl Disc (2014) 28: 808. doi:10.1007/s10618-013-0319-9


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. (Proceedings of Discovery Science, pp 278–289, 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. (Theor Comput Sci 411(3):691–700, 2010)”. We show how the interestingness of patterns of the proposed syntax can conveniently be quantified using a general framework for quantifying subjective interestingness of patterns “De Bie (Data Min Knowl Discov 23(3):407–446, 2011b)”. Finally, we illustrate the usefulness and the general applicability of our approach by discussing results on real-world and synthetic databases.


Multi-relational data mining Pattern mining Interestingness measures Maximum entropy modelling K-partite graphs 

Copyright information

© The Author(s) 2013

Authors and Affiliations

  1. 1.Intelligent Systems LaboratoryUniversity of BristolBristolUK
  2. 2.Fraunhofer IAISSchloss BirlinghovenSankt AugustinGermany

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