Clustering by integrating multi-objective optimization with weighted k-means and validity analysis

Authors:
Tansel Özyer;Reda Alhajj;Ken Barker
Affiliations:
Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada;Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada;Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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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.