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Interestingness Measures for Multi-Level Association Rules

By Gavin Shaw, Yue Xu and Shlomo Geva

Abstract

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

Topics: 080109 Pattern Recognition and Data Mining
Publisher: Springer International Publishing
Year: 2014
DOI identifier: 10.1007/978-3-319-01866-9_2
OAI identifier: oai:eprints.qut.edu.au:66335
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