Semi-supervised spectral clustering with automatic propagation of pairwise constraints

In our data driven world, clustering is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on ground truth to perform the classification and are usually subject to overtraining issues. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given the difficulty of the task, unsupervised learning tends to provide inferior results to supervised learning. A compromise is then to use learning only for some of the ambiguous classes, in order to boost performances. In this context, this paper studies the impact of pairwise constraints to unsupervised Spectral Clustering. We introduce a new generalization of constraint propagation which maximizes partitioning quality while reducing annotation costs. Experiments show the efficiency of the proposed scheme.