Semi-supervised clustering algorithms relies on side information, either labeled data (seeds) or pairwise constraints (must-link or cannot link) between data objects, to improve the quality of clustering. This paper proposes to extend an existing seed-based clustering algorithm with an active learning mechanism to collect pairwise constraints. My new semi-supervised algorithm can deal with both seeds and constraints. Experiment results on real data sets show the efficient of my algorithm when compared to the initial seed-based clustering algorithm. 

Tải file An efficient method for active semi-supervised density based clustering tại đây