Interesting pattern mining in multi-relational data

• Published in: Data mining and knowledge discovery Vol. 28; no. 3; pp. 808 - 849
• Main Authors: Spyropoulou, Eirini, De Bie, Tijl, Boley, Mario
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.05.2014; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Communities; Computer Science; Data mining; Data Mining and Knowledge Discovery; Genre; Information Storage and Retrieval; Logic programming; Pattern analysis; Physics; Relational data bases; Representations; Statistics for Engineering; Syntax; Tables (data); Maximum entropy modelling; Pattern mining; Multi-relational data mining; Interestingness measures; K-partite graphs
• ISSN: 1384-5810; 1573-756X
• 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.