Knowledge Discovery from Databases (KDD) association rules sensitivity value objective interestingness measures interestingness interval
Issue Date:
2008
Publisher:
ĐHQGHN
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
