Eliminating redundant association rules in multi-level datasets

Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2009) Eliminating redundant association rules in multi-level datasets. In 4th International Conference on Data Mining, 14-17 July 2008, Las Vegas, Nevada, USA.

Accepted Version (PDF 112kB)
Access restricted – pending publisher permission.

View at publisher


Association rule mining plays an important job in knowledge and information discovery and there are many approaches available. However, there are still shortcomings with the quality of the discovered rules. Often the number of the discovered rules is huge and many of them are redundant, 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. However, work by Pasquier et. al. [14] and Xu & Li [17,18] is only focused on single level datasets. In this paper, we propose an extension to this previous work that allows them to remove hierarchically redundant rules from multi-level datasets. We also show that the resulting concise representation of non-redundant association rules is lossless since all association rules can be derived from the representation. Experiments show that our extension can effectively generate multilevel non-redundant rules.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

502 since deposited on 03 Mar 2009
53 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 18454
Item Type: Conference Paper
Refereed: Yes
Additional Information: Access to the author-version is currently restricted pending permission from the publisher. For more information, please refer to the conference’s website (see hypertext link) or contact the author.
Keywords: Multi-level datasets, Association rules, Redundancy
ISBN: 1-60132-062-0
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 CSREA Press
Deposited On: 03 Mar 2009 22:28
Last Modified: 29 Feb 2012 13:47

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page