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Clustering by Integrating Multi-objective Optimization with Weighted K-Means and Validity Analysis.

Authors :
Corchado, Emilio
Hujun Yin
Botti, Vicente
Fyfe, Colin
Özyer, Tansel
Alhajj, Reda
Barker, Ken
Source :
Intelligent Data Engineering & Automated Learning - IDEAL 2006; 2006, p454-463, 10p
Publication Year :
2006

Abstract

This paper presents a clustering approach that integrates multi-objective optimization, weighted k-means and validity analysis in an iterative process to automatically estimate the number of clusters, and then partition the whole given data to produce the most natural clustering. The proposed approach has been tested on real-life dataset; results of both weighted and unweighed k-means are reported to demonstrate applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540454854
Database :
Complementary Index
Journal :
Intelligent Data Engineering & Automated Learning - IDEAL 2006
Publication Type :
Book
Accession number :
32914184
Full Text :
https://doi.org/10.1007/11875581_55