A Unified View of Objective Interestingness Measures 1st Edition by Céline Hébert, Bruno Crémilleux 9783540734987

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ISBN 13: 9783540734987

Author: Céline Hébert; Bruno Crémilleux

Association rule mining often results in an overwhelming number of rules. In practice, it is difficult for the final user to select the most relevant rules. In order to tackle this problem, various interestingness measures were proposed. Nevertheless, the choice of an appropriate measure remains a hard task and the use of several measures may lead to conflicting information. In this paper, we give a unified view of objective interestingness measures. We define a new framework embedding a large set of measures called SBMs and we prove that the SBMs have a similar behavior. Furthermore, we identify the whole collection of the rules simultaneously optimizing all the SBMs. We provide an algorithm to efficiently mine a reduced set of rules among the rules optimizing all the SBMs. Experiments on real datasets highlight the characteristics of such rules.

A Unified View of Objective Interestingness Measures 1st Table of contents:

1 Introduction
2 Preliminaries

3 A Formal Framework for Objective Measures: The Set
of Simultaneously Bounded Measures

4 SBMs’ Bounds and Behavior

5 Rule Mining

6 Experiments

7 Conclusion and Future Work

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