Interestingness Measures for Multi-Level Association Rules
Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2009) Interestingness Measures for Multi-Level Association Rules. In Proceedings of ADCS 2009, School of Information Technologies, University of Sydney, University of New South Wales, Sydney, Australia, pp. 27-34.
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 paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and
peculiarity can be used to help identify those rules from
multi-level datasets that are potentially useful.
Citation countsare sourced monthly fromand 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 theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays 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:||Information Retrieval, Interestingness Measures, Association Rules, Multi-Level Datasets|
|Subjects:||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 Information Technology
|Copyright Owner:||Copyright 2009 The authors|
|Deposited On:||19 Jan 2010 11:36|
|Last Modified:||01 Mar 2012 00:12|
Repository Staff Only: item control page