Clustering with Constraints: Feasibility Issues and the k-Means Algorithm

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Semi-supervised graph clustering: a kernel approach (2005, methodology): “…of semi-supervised clustering with constraints have been studied recently, including (1) image segmentation for object iden-tiﬁcation in Aibo robots (Davidson and Ravi 2005a), where cluster-level constraints are used to improve the pixel clustering; (2) object recognition in video sequences (Yan…”
CECM: Constrained evidential C-means algorithm (2012, positive): “Prior information can be exploited at different levels such as: the cluster level with, for instance, a minimum distance neighborhood [9], the model level with the requirement of balanced clusters [33] or the specification of non desired solutions [12], or at the instance level.”