Treffer: Multi-objective genetic algorithm based approach for optimizing fuzzy sequential patterns
Title:
Multi-objective genetic algorithm based approach for optimizing fuzzy sequential patterns
Authors:
Source:
16th IEEE international conference on tools with artificial intelligence ICTAI 2004 (Boca Raton Fl, 15-17 November 2004)Proceedings - International Conference on Tools with Artificial Intelligence, TAI. :396-400
Publisher Information:
Los Alamitos CA: IEEE, 2004.
Publication Year:
2004
Physical Description:
print, 18 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Algorithme flou, Fuzzy algorithm, Algoritmo borroso, Algorithme génétique, Genetic algorithm, Algoritmo genético, Association statistique, Statistical association, Asociación estadística, Base donnée, Database, Base dato, Confiance, Confidence, Confianza, Ensemble flou, Fuzzy set, Conjunto difuso, Fouille donnée, Data mining, Busca dato, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Méthode optimisation, Optimization method, Método optimización, Méthode séquentielle, Sequential method, Método secuencial, Optimisation, Optimization, Optimización, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Engineering Firat University, 23119, Elaziğ, Turkey
ADSA Lab, Department of Computer Science University of Calgary, Calgary, Alberta, Canada
ADSA Lab, Department of Computer Science University of Calgary, Calgary, Alberta, Canada
ISSN:
1082-3409
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19103862
Database:
PASCAL Archive
Weitere Informationen
This paper introduces the optimized sequential pattern problem and presents a novel approach to find such patterns. All the methods described in the literature to optimize association rules employ a single objective measure, such as optimized confidence or optimized support. In this study, we propose a novel multi-objective Genetic Algorithm (GA) based optimization method for optimizing quantitative sequential patterns. The objective measures of are support, confidence and a parameter related to the total number of fuzzy sets in the sequence. Experimental results on a synthetic database demonstrate the effectiveness and applicability of the proposed method.