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Utilizing non-redundant association rules from multi-level datasets

Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2009) Utilizing non-redundant association rules from multi-level datasets. In 1st WI-IAT Doctoral Workshop at the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 9 December 2008, Sydney, Australia.

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Abstract

Association rule mining and recommender systems are two popular methods for obtaining knowledge and information from datasets. However, both of these methods suffer from limitations. Traditionally association rule mining has focused on extracting as many rules as possible from flat datasets. More recently, issues over the number of rules and obtaining rules from datasets with multiple concept levels have come into focus. Recommender systems have been popular with users when it comes to helping find similar interests to those they already have. However, recommender systems suffer from two major problems, cold start and novelty.

The aims of our research is to develop an approach for extracting non-redundant multi-level and crosslevel association rules from datasets with multiple concept levels and utilise them in a recommender system with the aim of potentially solving the cold start and novelty problems.

Impact and interest:

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ID Code: 18459
Item Type: Conference Paper
Additional URLs:
Keywords: Multi-level datasets, Association rules, Redundancy, Recommender systems
DOI: 10.1109/WIIAT.2008.39
ISBN: 978-0-7695-3496-1
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Database Management (080604)
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 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 11:03
Last Modified: 29 Feb 2012 23:47

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