A classification-based approach to semi-supervised clustering with pairwise constraints.
In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification tasks: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method. The proposed approach is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method.
['Śmieja|Marek|M|', 'Struski|Łukasz|Ł|', 'Figueiredo|Mário A T|MAT|']
D016000:Cluster Analysis D016208:Databases, Factual D016571:Neural Networks, Computer D000069553:Supervised Machine Learning
Semi-supervised clustering Pairwise constraints Neural networks Siamese neural networks Deep learning