Semi-Supervised Clustering with User Feedback
We present an approach to clustering based on the observa- tion that "it is easier to criticize than to construct." Our approach of semi- supervised clustering allows a user to iteratively provide feedback to a clus- tering algorithm. The feedback is incorporated in the form of constraints, which the clustering algorithm attempts to satisfy on future iterations. These constraints allow the user to guide the clusterer toward clusterings of the data that the user finds more useful. We demonstrate semi-supervised clustering with a system that learns to cluster news stories from a Reuters data set. 1