Interestingness Measures for Multi-Level Association Rules

Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2014) Interestingness Measures for Multi-Level Association Rules. In Faucher, Colette & Jain, Lakhmi C. (Eds.) Innovations in Intelligent Machines-4 : Recent Advances in Knowledge Engineering. Springer International Publishing, pp. 47-74.

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Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach, which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this chapter we propose two approaches which measure multi-level association rules to help evaluate their interestingness by considering the database’s underlying taxonomy. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

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ID Code: 66335
Item Type: Book Chapter
DOI: 10.1007/978-3-319-01866-9_2
ISBN: 9783319018652 (Print) 9783319018669 (Online)
ISSN: 1860-949X
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Springer International Publishing Switzerland
Deposited On: 31 Mar 2014 00:58
Last Modified: 24 Jun 2017 14:36

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