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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOID. 207-216. ACM Press (1993)
Baclawski, K., Schneider, T.: The open ontology repository initiative: requirements and research challenges. In: Workshop on Collaborative Management of Structured Knowledge at the ISWC (2009)
Cherfi, H., Napoli, A., Toussaint, Y.: Towards a text mining methodology using association rule extraction. Int. J. Soft Comput. 10, 431–441 (2006)
Cornet, R., de Keizer, N.: Forty years of SNOMED: a literature review. BMC Med. Inf. Decis. Making 8(1), S2 (2008)
Fernandez, J., Gonzalez, J.: Hierarchical graph search for mobile robot path planning. In: IEEE ICRA, pp. 656–661 (1998)
Garbacz, P., Trypuz, R.: A metaontology for applied ontology. Appl. Ontol. 8, 1–30 (2013)
Herre, H., Loebe, F.: A meta-ontological architecture for foundational ontologies. On the Move to Meaningful Internet Systems, pp. 1398–1415 (2005)
Janetzko, D., Cherfi, H., Kennke, R., Napoli, A., Toussaint, Y.: Knowledge-based selection of association rules for text mining. In: ECAI, pp. 485–489 (2004)
Lieber, J., Napoli, A., Szathmary, L.: First elements on knowledge discovery guided by domain knowledge. Concept Lattices Appl. 4923, 22–41 (2008)
Marinica, C., Guillet, F.: Knowledge based interactive postmining of association rules using ontologies. IEEE Trans. Knowl. Data Eng. 22(6), 784–797 (2010)
Palma, P., Hartmann, J., Haase, P.: Ontology metadata vocabulary for the semantic web. Technical Report, Universidad Politcnica de Madrid, University of Karlsruhe (2008)
Ramesh, C., Ramana, K., Rao, K., Sastry, C.: Interactive post-mining association rules using cost complexity pruning and ontologies KDD. Int. J. Comput. Appl. 68(20), 16–21 (2013)
Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: VLDB, pp. 432–444 (1995)
Singh, L., Chen, B., Haight, R., Scheuermann, P.: An algorithm for constrained association rule mining in semi-structured data. In: PAKDD, pp. 148–158 (1999)
Song, S., Kim, E., Kim, H., Kumar, H.: Query-based association rule mining supporting user perspective. Computing 93, 1–25 (2011)
Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: KDD, pp. 67–73 (1997)
Tan, P., Steinbach, M.: Introduction to Data Mining. Addison-Wesley (2006)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-47175-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47174-7
Online ISBN: 978-3-319-47175-4
eBook Packages: Computer ScienceComputer Science (R0)