Measuring the accuracy and interest of association rules: A new framework
Fernando Berzal, Ignacio José Blanco, Daniel Sánchez, M.A. Vila · Intelligent Data Analysis · 2002
It has been pointed out that the usual framework to assess association rules, based on support and confidence as measures of importance and accuracy, has several drawbacks. In particular, the presence of items with very high support can lead to obtain many misleading rules, even in the order of 95% 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, and we discuss some intuitive properties of the new framework. Both the theoretical properties and the experiments we have performed show that we can avoid the discovery of misleading rules, improving the manageability and quality of the results.