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Interesting pattern mining in multi-relational data

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Abstract
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.
Keywords
Interestingness measures, Maximum entropy modelling, Pattern mining, Multi-relational data mining, CLIQUES, DATABASES, EFFICIENT ALGORITHM, DISCOVERY, K-partite graphs

Citation

Please use this url to cite or link to this publication:

MLA
Spyropoulou, Eirini, Tijl De Bie, and Mario Boley. “Interesting Pattern Mining in Multi-relational Data.” DATA MINING AND KNOWLEDGE DISCOVERY 28.3 (2014): 808–849. Print.
APA
Spyropoulou, E., De Bie, T., & Boley, M. (2014). Interesting pattern mining in multi-relational data. DATA MINING AND KNOWLEDGE DISCOVERY, 28(3), 808–849.
Chicago author-date
Spyropoulou, Eirini, Tijl De Bie, and Mario Boley. 2014. “Interesting Pattern Mining in Multi-relational Data.” Data Mining and Knowledge Discovery 28 (3): 808–849.
Chicago author-date (all authors)
Spyropoulou, Eirini, Tijl De Bie, and Mario Boley. 2014. “Interesting Pattern Mining in Multi-relational Data.” Data Mining and Knowledge Discovery 28 (3): 808–849.
Vancouver
1.
Spyropoulou E, De Bie T, Boley M. Interesting pattern mining in multi-relational data. DATA MINING AND KNOWLEDGE DISCOVERY. 2014;28(3):808–49.
IEEE
[1]
E. Spyropoulou, T. De Bie, and M. Boley, “Interesting pattern mining in multi-relational data,” DATA MINING AND KNOWLEDGE DISCOVERY, vol. 28, no. 3, pp. 808–849, 2014.
@article{6936358,
  abstract     = {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.},
  author       = {Spyropoulou, Eirini and De Bie, Tijl and Boley, Mario},
  issn         = {1384-5810},
  journal      = {DATA MINING AND KNOWLEDGE DISCOVERY},
  keywords     = {Interestingness measures,Maximum entropy modelling,Pattern mining,Multi-relational data mining,CLIQUES,DATABASES,EFFICIENT ALGORITHM,DISCOVERY,K-partite graphs},
  language     = {eng},
  number       = {3},
  pages        = {808--849},
  title        = {Interesting pattern mining in multi-relational data},
  url          = {http://dx.doi.org/10.1007/s10618-013-0319-9},
  volume       = {28},
  year         = {2014},
}

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