Constrained Spectral Clustering under a Local Proximity Structure Assumption

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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.