Constraint Programming for Multi-criteria Conceptual Clustering - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Constraint Programming for Multi-criteria Conceptual Clustering

Résumé

A conceptual clustering is a set of formal concepts (i.e., closed itemsets) that defines a partition of a set of transactions. Finding a conceptual clustering is an N P-complete problem for which Constraint Programming (CP) and Integer Linear Programming (ILP) approaches have been recently proposed. We introduce new CP models to solve this problem: a pure CP model that uses set constraints, and an hybrid model that uses a data mining tool to extract formal concepts in a preprocessing step and then uses CP to select a subset of formal concepts that defines a partition. We compare our new models with recent CP and ILP approaches on classical machine learning instances. We also introduce a new set of instances coming from a real application case, which aims at extracting setting concepts from an Enterprise Resource Planning (ERP) software. We consider two classic criteria to optimize, i.e., the frequency and the size. We show that these criteria lead to extreme solutions with either very few small formal concepts or many large formal concepts, and that compromise clusterings may be obtained by computing the Pareto front of non dominated clusterings.
Fichier principal
Vignette du fichier
main.pdf (481.34 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01544239 , version 1 (21-06-2017)

Identifiants

  • HAL Id : hal-01544239 , version 1

Citer

Maxime Chabert, Christine Solnon. Constraint Programming for Multi-criteria Conceptual Clustering. 23rd International Conference on Principles and Practice of Constraint Programming (CP), Aug 2017, Melbourne, Australia. pp.460-476. ⟨hal-01544239⟩
167 Consultations
350 Téléchargements

Partager

Gmail Facebook X LinkedIn More