Interestingness Measures for Multi-Level Association Rules

Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2009) Interestingness Measures for Multi-Level Association Rules. In Proceedings of ADCS 2009, School of Information Technologies, University of Sydney, University of New South Wales, Sydney, Australia, pp. 27-34.

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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 paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. 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: 29741
Item Type: Conference Paper
Refereed: Yes
Keywords: Information Retrieval, Interestingness Measures, Association Rules, Multi-Level Datasets
ISBN: 9781742101712
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems not elsewhere classified (080699)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Information Technology
Copyright Owner: Copyright 2009 The authors
Deposited On: 19 Jan 2010 01:36
Last Modified: 29 Feb 2012 14:12

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