Topic-Constrained Hierarchical Clustering for Document Datasets

In this paper, we propose the topic-constrained hierarchical clustering, which organizes document datasets into hierarchical trees consistant with a given set of topics. The proposed algorithm is based on a constrained agglomerative clustering framework and a semi-supervised criterion function that emphasizes the relationship between documents and topics and the relationship among documents themselves simultaneously. The experimental evaluation show that our algorithm outperformed the traditional agglomerative algorithm by 7.8% to 11.4%.