A new nonsmooth optimization algorithm for minimum sum-of-squares clustering problems

The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the former problem based on nonsmooth optimization techniques is developed. The issue of applying this algorithm to large data sets is discussed. Results of num...

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Veröffentlicht in:	European journal of operational research 2006-04, Vol.170 (2), p.578-596
Hauptverfasser:	Bagirov, Adil M., Yearwood, John
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Cluster analysis Exact sciences and technology Mathematical programming Minimum sum-of-squares clustering Nonsmooth optimization Operational research and scientific management Operational research. Management science Operations research Optimization algorithms Optimization techniques Studies
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Beschreibung
Zusammenfassung:	The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the former problem based on nonsmooth optimization techniques is developed. The issue of applying this algorithm to large data sets is discussed. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithm.
ISSN:	0377-2217 1872-6860
DOI:	10.1016/j.ejor.2004.06.014