Flexible constrained spectral clustering

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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...
Xiang Wang, Ian Davidson
Added 30 Aug 2010
Updated 30 Aug 2010
Type Conference
Year 2010
Where KDD
Authors Xiang Wang, Ian Davidson
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