Deriving non-redundant approximate association rules from hierarchical datasets

Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2009) Deriving non-redundant approximate association rules from hierarchical datasets. In 17th ACM Conference on Information and Knowledge Management, 26-30 October 2008, Napa Valley, USA.

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Association rule mining plays an important job in knowledge and information discovery. However, there are still shortcomings with the quality of the discovered rules and often the number of discovered rules is huge and contain redundancies, especially in the case of multi-level datasets. Previous work has shown that the mining of non-redundant rules is a promising approach to solving this problem, with work by [6,8,9,10] focusing on single level datasets. Recent work by Shaw et. al. [7] has extended the nonredundant approaches presented in [6,8,9] to include the elimination of redundant exact basis rules from multi-level datasets. Here we propose a continuation of the work in [7] that allows for the removal of hierarchically redundant approximate basis rules from multi-level datasets by using a dataset’s hierarchy or taxonomy.

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ID Code: 18455
Item Type: Conference Item (Poster)
Refereed: Yes
Additional URLs:
Keywords: Multi-level datasets, Association rules, Redundancy
DOI: 10.1145/1458082.1458328
ISBN: 978-1-59593-991-3
Subjects: 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 Software Engineering & Data Communications
Copyright Owner: Copyright 2009 [please consult the authors]
Deposited On: 03 Mar 2009 22:55
Last Modified: 29 Feb 2012 13:47

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