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.
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Özyer, T., Alhajj, R., Barker, K. (2006). Clustering by Integrating Multi-objective Optimization with Weighted K-Means and Validity Analysis. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_55
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DOI: https://doi.org/10.1007/11875581_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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