Interestingness Measures for Multi-Level Association Rules


Autoria(s): Shaw, Gavin; Xu, Yue; Geva, Shlomo
Data(s)

04/12/2009

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/29741/

Publicador

School of Information Technologies, University of Sydney

Relação

http://eprints.qut.edu.au/29741/2/29741.pdf

http://es.csiro.au/adcs2009/proceedings/

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.

Direitos

Copyright 2009 The authors

Fonte

Faculty of Science and Technology; School of Information Technology

Palavras-Chave #080699 Information Systems not elsewhere classified #080704 Information Retrieval and Web Search #Information Retrieval #Interestingness Measures #Association Rules #Multi-Level Datasets
Tipo

Conference Paper