Nonsmooth Optimization-Based Model and Algorithm for Semisupervised Clustering

Using a nonconvex nonsmooth optimization approach, we introduce a model for semisupervised clustering (SSC) with pairwise constraints. In this model, the objective function is represented as a sum of three terms: the first term reflects the clustering error for unlabeled data points, the second term...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2023-09, Vol.34 (9), p.5517-5530
Hauptverfasser:	Bagirov, Adil M., Taheri, Sona, Bai, Fusheng, Zheng, Fangying
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive algorithms Algorithms Cluster analysis Clustering Clustering algorithms Constraint modelling Costs Data points discrete gradient method (DGM) Errors Iterative methods Linear programming Measurement nonsmooth optimization Objective function Optimization pairwise constraints Partitioning algorithms semisupervised clustering (SSC) Semisupervised learning
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