Extracting non-redundant approximate rules from multi-level datasets
Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2009) Extracting non-redundant approximate rules from multi-level datasets. In 20th International Conference on Tools with Artificial Intelligence, 3-5 November 2008, Dayton, Ohio, USA.
Association rule mining plays an important job in knowledge and information discovery. Often the number of the discovered rules is huge and many of them are redundant, especially for multi-level datasets. Previous work has shown that the mining of non-redundant rules is a promising approach to solving this problem, with work in [14,18,19,20] focusing on single level datasets. Recent work by Shaw et. al.  has extended the non-redundant approaches presented in [14,18,19] to include the elimination of redundant exact basis rules from multilevel datasets. In this paper, we propose an extension to the work in [14,15,18,19,20] to allow for the removal of hierarchically redundant approximate basis rules from multi-level datasets through the use of the dataset’s hierarchy or taxonomy. Experimentation shows our approach can effectively generate both multi-level and cross level non-redundant rule sets which are lossless.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
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.
|Item Type:||Conference Paper|
|Keywords:||Multi-level datasets, Association rules, Redundancy|
|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 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||04 Mar 2009 00:45|
|Last Modified:||29 Feb 2012 13:47|
Repository Staff Only: item control page