Comparing rule measures for predictive association rules

• Type: Chapter or Part of a Book
• Date: 2007

Authors

Paulo J Azevedo (Author) Other; Alipio M Jorge (Author) FEP

Book

Title: Machine Learning: ECML 2007

Pages: 510-517

Indexing

ISI Web of Knowledge - 43 Citations

Scopus - 56 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

Notes: 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. of pages: 8