Enhancing semi-supervised document clustering with feature supervision
Yeming Hu, Evangelos Milios, James Blustein · 2012
Traditional semi-supervised clustering uses only limited user supervision in the form of labeled instances and pairwise instance constraints to aid unsupervised clustering. However, user supervision can also be provided in alternative forms for document clustering, such as labeling a feature by indicating whether it discriminates among clusters. This paper thus fills this void by enhancing traditional semi-supervised clustering with feature supervision which asks the user to label discriminating features during labeling the instance or pairwise instance constraints. Various types of semi-supervised clustering algorithms were explored with feature supervision. Our experimental results on several real-world datasets demonstrate that augmenting the instance-level supervision with feature-level supervision can significantly improve document clustering performance.