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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

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MLA
Spyropoulou, Eirini, et al. “Interesting Pattern Mining in Multi-Relational Data.” DATA MINING AND KNOWLEDGE DISCOVERY, vol. 28, no. 3, 2014, pp. 808–49, doi:10.1007/s10618-013-0319-9.
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. https://doi.org/10.1007/s10618-013-0319-9
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): 808–49. https://doi.org/10.1007/s10618-013-0319-9.
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. doi:10.1007/s10618-013-0319-9.
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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