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International Conference on Innovative Techniques and Applications of Artificial Intelligence

SGAI 2016: Research and Development in Intelligent Systems XXXIII pp 21–35Cite as

Category-Driven Association Rule Mining

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Abstract

The quality of rules generated by ontology-driven association rule mining algorithms is constrained by the algorithm’s effectiveness in exploiting the usually large ontology in the mining process. We present a framework built around superimposing a hierarchical graph structure on a given ontology to divide the rule mining problem into disjoint subproblems whose solutions can be iteratively joined to find global associations. We present a new metric for evaluating the interestingness of generated rules based on where their constructs fall within the ontology. Our metric is anti-monotonic on subsets, making it usable in an Apriori-like algorithm which we present here. The algorithm categorises the ontology into disjoint subsets utilising the hierarchical graph structure and uses the metric to find associations in each, joining the results using the guidance of anti-monotonicity. The algorithm optionally embeds built-in definitions of user-specified filters to reflect user preferences. We evaluate the resulting model using a large collection of patient health records.

Keywords

  • Association rule mining
  • Ontologies
  • Big data

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Notes

  1. 1.

    https://bioportal.bioontology.org/ontologies/SCTTO

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Acknowledgments

The authors would like to acknowledge the National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and Kings College London.

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Authors and Affiliations

  1. Department of Biostatistics and Health Informatics, King’s College London, London, UK

    Zina M. Ibrahim, Honghan Wu & Richard J. B. Dobson

  2. The South London and Maudsley NHS Foundation Trust, London, UK

    Robbie Mallah

Authors
  1. Zina M. Ibrahim
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  2. Honghan Wu
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  3. Robbie Mallah
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  4. Richard J. B. Dobson
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Corresponding author

Correspondence to Zina M. Ibrahim .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, Hampshire, United Kingdom

    Max Bramer

  2. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, East Sussex, United Kingdom

    Miltos Petridis

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Ibrahim, Z.M., Wu, H., Mallah, R., Dobson, R.J.B. (2016). Category-Driven Association Rule Mining. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-47175-4_2

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