Result: Novel clustering approach that employs genetic algorithm with new representation scheme and multiple objectives
Title:
Novel clustering approach that employs genetic algorithm with new representation scheme and multiple objectives
Authors:
Source:
DaWaK 2004 : data warehousing and knowledge discovery (Zaragoza, 1-3 September 2004)Lecture notes in computer science. :219-228
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Subject Terms:
Documentation, 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, Systèmes d'information. Bases de données, Information systems. Data bases, Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse amas, Cluster analysis, Analisis cluster, Classification, Clasificación, Découverte connaissance, Knowledge discovery, Descubrimiento conocimiento, Entrepôt donnée, Data warehouse, Almacen dato, Forme linéaire, Linear form, Forma lineal, Fouille donnée, Data mining, Busca dato, Graphe linéaire, Linear graph, Grafo lineal, Optimum Pareto, Pareto optimum, Optimo Pareto, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Variation totale, Total variation, Variación total
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
ISSN:
0302-9743
Rights:
Copyright 2004 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.16144044
Database:
PASCAL Archive
Further Information
In this paper, we propose a new encoding scheme for GA and employ multiple objectives in handling the clustering problem. The proposed encoding scheme uses links so that objects to be clustered form a linear pseudo-graph. As multiple objectives are concerned, we used two objectives: 1) to minimize the Total Within Cluster Variation (TWCV); and 2) minimizing the number of clusters in a partition. Our approach obtains the optimal partitions for all the possible numbers of clusters in the Pareto Optimal set returned by a single GA run. The performance of the proposed approach has been tested using two well-known data sets: Iris and Ruspini. The obtained results demonstrate improvement over classical approaches.