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Mining interesting patterns in multi-relational data with N-ary relationships

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Organization
Abstract
We present a novel method for mining local patterns from multi-relational data in which relationships can be of any arity. More specifically, we define a new pattern syntax for such data, develop an efficient algorithm for mining it, and define a suitable interestingness measure that is able to take into account prior information of the data miner. Our approach is a strict generalisation of prior work on multi-relational data in which relationships were restricted to be binary, as well as of prior work on local pattern mining from a single n-ary relationship. Remarkably, despite being more general our algorithm is comparably fast or faster than the state-of-the-art in these less general problem settings.
Keywords
DISCOVERY

Citation

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

MLA
Spyropoulou, Eirini, Tijl De Bie, and Mario Boley. “Mining Interesting Patterns in Multi-relational Data with N-ary Relationships.” Lecture Notes in Artificial Intelligence. Vol. 8140. 2013. 217–232. Print.
APA
Spyropoulou, E., De Bie, T., & Boley, M. (2013). Mining interesting patterns in multi-relational data with N-ary relationships. Lecture Notes in Artificial Intelligence (Vol. 8140, pp. 217–232). Presented at the 16th International Conference on Discovery Science (DS).
Chicago author-date
Spyropoulou, Eirini, Tijl De Bie, and Mario Boley. 2013. “Mining Interesting Patterns in Multi-relational Data with N-ary Relationships.” In Lecture Notes in Artificial Intelligence, 8140:217–232.
Chicago author-date (all authors)
Spyropoulou, Eirini, Tijl De Bie, and Mario Boley. 2013. “Mining Interesting Patterns in Multi-relational Data with N-ary Relationships.” In Lecture Notes in Artificial Intelligence, 8140:217–232.
Vancouver
1.
Spyropoulou E, De Bie T, Boley M. Mining interesting patterns in multi-relational data with N-ary relationships. Lecture Notes in Artificial Intelligence. 2013. p. 217–32.
IEEE
[1]
E. Spyropoulou, T. De Bie, and M. Boley, “Mining interesting patterns in multi-relational data with N-ary relationships,” in Lecture Notes in Artificial Intelligence, Singapore, Singapore, 2013, vol. 8140, pp. 217–232.
@inproceedings{6936384,
  abstract     = {{We present a novel method for mining local patterns from multi-relational data in which relationships can be of any arity. More specifically, we define a new pattern syntax for such data, develop an efficient algorithm for mining it, and define a suitable interestingness measure that is able to take into account prior information of the data miner. Our approach is a strict generalisation of prior work on multi-relational data in which relationships were restricted to be binary, as well as of prior work on local pattern mining from a single n-ary relationship. Remarkably, despite being more general our algorithm is comparably fast or faster than the state-of-the-art in these less general problem settings.}},
  author       = {{Spyropoulou, Eirini and De Bie, Tijl and Boley, Mario}},
  booktitle    = {{Lecture Notes in Artificial Intelligence}},
  isbn         = {{978-3-642-40897-7}},
  issn         = {{0302-9743}},
  keywords     = {{DISCOVERY}},
  language     = {{eng}},
  location     = {{Singapore, Singapore}},
  pages        = {{217--232}},
  title        = {{Mining interesting patterns in multi-relational data with N-ary relationships}},
  volume       = {{8140}},
  year         = {{2013}},
}

Web of Science
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