Constrained Spectral Clustering under a Local Proximity Structure Assumption

This work focuses on incorporating pairwise constraints into a spectral clustering algorithm. A new constrained spectral clustering method is proposed, as well as an active constraint acquisition technique and a heuristic for parameter selection. We demonstrate that our constrained spectral clustering method, CSC, works well when the data exhibits what we term local proximity structure. Empirical results on synthetic and real data sets show that CSC outperforms two other constrained clustering algorithms.