PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Objective Novelty of Association Rules: Measuring the Confidence Boost
José Balcázar
In: EGC 2010, 26-29 jan 2010, Hamamet, Tunisia.

Abstract

In association rule mining, it is well-known that merely imposing an absolute confidence threshold leads to certain shortcomings. Many alternative proposals have been suggested to overcome them. Here we propose, instead, to complement the process by filtering also the obtained rules according to their novelty, measured in a relative way with respect to the confidences of stronger rules from the same dataset. Our proposal, the confidence boost of a rule, encompasses two previous similar notions (confidence width and rule blocking) from previous works.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5797
Deposited By:José Balcázar
Deposited On:08 March 2010