A confidence-based active approach for semi-supervised hierarchical clustering

Semi-supervised approaches have proven to be effective in clustering tasks. They allow user input, thus improving the quality of the clustering obtained, while maintaining a controllable level of user intervention. Despite being an important class of algorithms, hierarchical clustering has been little explored in semisupervised solutions. In this report, we address the problem of semi-supervised hierarchical clustering by using an active clustering solution with cluster-level constraints. This active learning approach is based on a new concept of merge confidence in an agglomerative clustering process. When there is lower confidence in a cluster merge the user can be queried and provide a clusterlevel constraint. The proposed method was compared with a unsupervised algorithm (average-link) and a semi-supervised algorithm based on pairwise constraints. The results show that our algorithm tends to be better than the pairwise constrained algorithm and can achieve a significant improvement when compared to the unsupervised algorithm.