Treffer: The k-means range algorithm for personalized data clustering in e-commerce : Human centered processes: Toward a naturalistic desicion making paradigm
Title:
The k-means range algorithm for personalized data clustering in e-commerce : Human centered processes: Toward a naturalistic desicion making paradigm
Source:
European journal of operational research. 177(3):1400-1408
Publisher Information:
Amsterdam: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 17 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Théorie de la décision. Théorie de l'utilité, Decision theory. Utility theory, 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, Infection, Infección, Virose, Viral disease, Virosis, Achat, Purchases, Compra, Amas, Cluster, Montón, Analyse donnée, Data analysis, Análisis datos, Base donnée spatiale, Spatial database, Base dato especial, Classification, Clasificación, Commande multimodèle, Multimodel control, Control multimodelo, Commerce électronique, Electronic trade, Comercio electronico, Méthode arborescente, Tree structured method, Método arborescente, Méthode heuristique, Heuristic method, Método heurístico, Prise décision, Decision making, Toma decision, Préférence, Preference, Preferencia, SIDA, AIDS, Structure 3 dimensions, Three dimensional structure, Estructura 3 dimensiones, Structure arborescente, Tree structure, Estructura arborescente, Structure donnée, Data structure, Estructura datos, Système aide décision, Decision support system, Sistema ayuda decisíon, Temps réel, Real time, Tiempo real, Data clustering, Distributed consumer decision-making, Heuristics, Personalized systems, Range search
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Management Science and Technology, Athens University of Economics and Business, 47A Evelpidon & 33 Lefkados Sir, Athens 11 362, Greece
Computer Science Department, State University of New York at Stony Brook, Stony Brook, NY 11794-4400, United States
Computer Science Department, State University of New York at Stony Brook, Stony Brook, NY 11794-4400, United States
ISSN:
0377-2217
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
Operational research. Management
Operational research. Management
Accession Number:
edscal.18410988
Database:
PASCAL Archive
Weitere Informationen
This paper describes the k-means range algorithm, a combination of the partitional k-means clustering algorithm with a well known spatial data structure, namely the range tree, which allows fast range searches. It offers a real-time solution for the development of distributed interactive decision aids in e-commerce since it allows the consumer to model his preferences along multiple dimensions, search for product information, and then produce the data clusters of the products retrieved to enhance his purchase decisions. This paper also discusses the implications and advantages of this approach in the development of on-line shopping environments and consumer decision aids in traditional and mobile e-commerce applications.