Metrics to support the evaluation of association rule clustering
Nenhuma Miniatura disponível
Data
2013-09-26
Autores
De Carvalho, Veronica Oliveira [UNESP]
Dos Santos, Fabiano Fernandes
Rezende, Solange Oliveira
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.
Descrição
Palavras-chave
Association Rules, Clustering, Pre-processing, Association mining, Pre-processing step, Research communities, Suitable solutions, Data warehouses, Association rules
Como citar
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8057 LNCS, p. 248-259.