Comparing rule measures for predictive association rules

• Tipo: Capítulo ou Parte de Livro
• Data: 2007

Autores

Paulo J Azevedo (Autor) Outra; Alipio M Jorge (Autor) FEP

Livro

Título: Machine Learning: ECML 2007

Páginas: 510-517

Indexação

ISI Web of Knowledge - 43 Citações

Scopus - 56 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-004-DVF

DOI: 10.1007/978-3-540-74958-5_47

Abstract (EN): We study the predictive ability of some association rule measures typically used to assess descriptive interest. Such measures, namely conviction, lift and chi(2) are compared with confidence, Laplace, mutual information, cosine, Jaccard and phi-coefficient. As prediction models, we use sets of association rules. Classification is done by selecting the best rule, or by weighted voting. We performed an evaluation on 17 datasets with different characteristics and conclude that conviction is on average the best predictive measure to use in this setting. We also provide some meta-analysis insights for explaining the results.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Notas: Print ISBN: 978-3-540-74957-8 Online ISBN: 978-3-540-74958-5 Part of the Lecture Notes in Computer Science book series (LNCS, volume 4701)

Nº de páginas: 8