Semi-Supervised document clustering and employing limited prior knowledge to aid in unsupervised clustering, have recently become a topic of significant interest to data mining and machine learning communities. Because receiving supervised data may be expensive, it is important to attain the most informative knowledge to improve the clustering performance. This paper presents a semi-supervised document clustering algorithm with active learning for pairwise constraints, aiming at getting improved clustering performance. The semi-supervised document clustering algorithm is a constrained DBSCAN (cons-DBSCAN) algorithm, which incorporates pairwise constraints to guide the clustering process in DBSCAN. Basing on measure of constraint set utility and analysis of DBSCAN algorithm, an active learning approach is proposed to select informative document pairs for obtaining user feedbacks. Experimental results show that this proposed approach is effective in document clustering. The clustering performance of active Cons-DBSCAN has dramatically improved with selected pairwise constraints. Moreover, the proposed approach performs better than the two representative methods.