Knowledge Discovery from Databases (KDD) association rules sensitivity value objective interestingness measures interestingness interval
Issue Date:
2008
Citation:
VNU Journal of Science, Natural Sciences and Technology 24 (2008) 122-132
Abstract:
In this paper, we propose a new approach to evaluate the behavior of objective interestingness measures on association rules. The objective interestingness measures are ranked according to the most significant interestingness interval calculated from an inversely cumulative distribution. The sensitivity values are determined by this interval in observing the rules having the highest interestingness values. The results will help the user (a data analyst) to have an insight view on the behaviors of objective interestingness measures and as a final purpose, to select the hidden knowledge in a rule set or a set of rule sets represented in the form of the most interesting rules.
