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Direct mining of subjectively interesting relational patterns

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Abstract
Data is typically complex and relational. Therefore, the development of relational data mining methods is an increasingly active topic of research. Recent work has resulted in new formalisations of patterns in relational data and in a way to quantify their interestingness in a subjective manner, taking into account the data analyst's prior beliefs about the data. Yet, a scalable algorithm to find such most interesting patterns is lacking. We introduce a new algorithm based on two notions: (1) the use of Constraint Programming, which results in a notably shorter development time, faster runtimes, and more flexibility for extensions such as branch-and-bound search, and (2), the direct search for the most interesting patterns only, instead of exhaustive enumeration of patterns before ranking them. Through empirical evaluation, we find that our novel bounds yield speedups up to several orders of magnitude, especially on dense data with a simple schema. This makes it possible to mine the most subjectively-interesting relational patterns present in databases where this was previously impractical or impossible.
Keywords
Data mining, Relational databases

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

Chicago
Guns, Tias, Achille Aknin, Jefrey Lijffijt, and Tijl De Bie. 2016. “Direct Mining of Subjectively Interesting Relational Patterns.” In 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) , 913–918. Institute of Electrical and Electronics Engineers (IEEE).
APA
Guns, T., Aknin, A., Lijffijt, J., & De Bie, T. (2016). Direct mining of subjectively interesting relational patterns. 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) (pp. 913–918). Presented at the 16th IEEE International Conference on Data Mining (ICDM) , Institute of Electrical and Electronics Engineers (IEEE).
Vancouver
1.
Guns T, Aknin A, Lijffijt J, De Bie T. Direct mining of subjectively interesting relational patterns. 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) . Institute of Electrical and Electronics Engineers (IEEE); 2016. p. 913–8.
MLA
Guns, Tias, Achille Aknin, Jefrey Lijffijt, et al. “Direct Mining of Subjectively Interesting Relational Patterns.” 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) . Institute of Electrical and Electronics Engineers (IEEE), 2016. 913–918. Print.
@inproceedings{8510594,
  abstract     = {Data is typically complex and relational. Therefore, the development of relational data mining methods is an increasingly active topic of research. Recent work has resulted in new formalisations of patterns in relational data and in a way to quantify their interestingness in a subjective manner, taking into account the data analyst's prior beliefs about the data. Yet, a scalable algorithm to find such most interesting patterns is lacking. We introduce a new algorithm based on two notions: (1) the use of Constraint Programming, which results in a notably shorter development time, faster runtimes, and more flexibility for extensions such as branch-and-bound search, and (2), the direct search for the most interesting patterns only, instead of exhaustive enumeration of patterns before ranking them. Through empirical evaluation, we find that our novel bounds yield speedups up to several orders of magnitude, especially on dense data with a simple schema. This makes it possible to mine the most subjectively-interesting relational patterns present in databases where this was previously impractical or impossible.},
  author       = {Guns, Tias and Aknin, Achille and Lijffijt, Jefrey and De Bie, Tijl},
  booktitle    = {2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) },
  isbn         = {978-1-5090-5473-2},
  issn         = {1550-4786},
  language     = {eng},
  location     = {Barcelona},
  pages        = {913--918},
  publisher    = {Institute of Electrical and Electronics Engineers (IEEE)},
  title        = {Direct mining of subjectively interesting relational patterns},
  url          = {http://dx.doi.org/10.1109/icdm.2016.0112},
  year         = {2016},
}

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