Flexible constrained spectral clustering

Constrained clustering has been well-studied for algorithms like K-means and hierarchical agglomerative clustering. However, how to encode constraints into spectral clustering remains a developing area. In this paper, we propose a flexible and generalized framework for constrained spectral clustering. In contrast to some previous efforts that implicitly encode Must-Link and Cannot-Link constraints by modifying the graph Laplacian or the resultant eigenspace, we present a more natural and principled formulation, which preserves the original graph Laplacian and explicitly encodes the constraints. Our method offers several practical advantages: it can encode the degree of belief (weight) in MustLink and Cannot-Link constraints; it guarantees to lowerbound how well the given constraints are satisfied using a user-specified threshold; and it can be solved deterministically in polynomial time through generalized eigendecomposition. Furthermore, by inheriting the objective function from...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Xiang Wang, Ian Davidson
• Keywords: Constrained Clustering, Constrained Spectral Clustering, Data Mining, KDD 2010, Spectral Clustering