Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6389
Title: Clustering by integrating multi-objective optimization with weighted K-means and validity analysis
Authors: Ozyer, Tansel
Alhajj, Reda
Barker, Ken
Keywords: [No Keywords]
Issue Date: 2006
Publisher: Springer-Verlag Berlin
Source: 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006) -- SEP 20-23, 2006 -- Univ Burgos, Burgos, SPAIN
Series/Report no.: LECTURE NOTES IN COMPUTER SCIENCE
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.
URI: https://hdl.handle.net/20.500.11851/6389
ISBN: 3-540-45485-3
ISSN: 0302-9743
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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