Learning Characteristic Rules Relying on Quantified Paths

In this paper, we address the characterization task and we present a general framework for the characterization of a target set of objects by means of their own properties, but also the properties of objects linked to them. According to the kinds of objects, various links can be considered. For instance, in the case of relational databases, associations are the straightforward links between pairs of tables. We propose , a new algorithm for mining characterization rules and we show how it can be used on multi-relational and spatial databases.

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Field Value
Source 7th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD)
Author Turmeaux, Teddy, Salleb, Ansaf, Vrain, Christel, Cassard, Daniel
Maintainer CCSD
Last Updated May 10, 2026, 05:02 (UTC)
Created May 10, 2026, 05:02 (UTC)
Identifier hal-00084885
Language en
contributor Laboratoire d'Informatique Fondamentale d'Orléans (LIFO) ; Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges
creator Turmeaux, Teddy
date 2003-05-10T00:00:00
harvest_object_id f241eb8f-9d5b-4de6-81fb-d25a2c102f0f
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-08-12T00:00:00
relation info:eu-repo/semantics/altIdentifier/doi/10.1007/b13634
set_spec type:COMM