A New Framework to Assess Association Rules

Autor:	Fernando Berzal, Daniel Sánchez, María Amparo Vila Miranda, Ignacio J. Blanco
Rok vydání:	2001
Předmět:	Association rule learning Order (exchange) Computer science business.industry media_common.quotation_subject Artificial intelligence Certainty Machine learning computer.software_genre business computer media_common
Zdroj:	Advances in Intelligent Data Analysis ISBN: 9783540425816 IDA
DOI:	10.1007/3-540-44816-0_10
Popis:	The usual support/confidence framework to assess association rules has several drawbacks that lead to obtain many misleading rules, even in the order of 95% of the discovered rules in some of our experiments. In this paper we introduce a different framework, based on Shortliffe and Buchanan's certainty factors and the new concept of very strong rules. The new framework has several good properties, and our experiments have shown that it can avoid the discovery of misleading rules.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::041a894844e2b99b7f49325aef9b1972 https://doi.org/10.1007/3-540-44816-0_10 Zobrazit plný text záznamu