Authors:
Fabrizio Angiulli
;
Fabio Fassetti
;
Luigi Palopoli
and
Domenico Trimboli
Affiliation:
DEIS, University of Calabria, Italy
Keyword(s):
Data mining, Rule induction, Exceptional properties.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Assume a population partitioned in two subpopulations, e.g. a set of normal individuals and a set of abnormal individuals, is given. Assume, moreover, that we look for a characterization of the reasons discriminating one subpopulation from the other. In this paper, we provide a technique by which such an evidence can be mined, by introducing the notion of discriminating rule, that is a kind of logical implication which is much more valid in one of the two subpopulations than in the other one. In order to avoid mining a potentially huge number of (not necessarily interesting) rule, we define a preference relationship among rules and exploit a suitable graph encoding in order to single out the most interesting ones, which we call outstanding rules. We provide an algorithm for detecting the outstanding discriminating rules and present experimental results obtained by applying the technique in several scenarios.