Spectral clustering with inconsistent advice

Tom Coleman, James Saunderson, Anthony Wirth

Abstract: Clustering with advice (often known as constrained clustering) has been a recent focus of the data mining community. Success has been achieved incorporating advice into the k-means and spectral clustering frameworks. Although the theory community has explored inconsistent advice, it has not yet been incorporated into spectral clustering. Extending work of De Bie and Cristianini, we set out a framework for finding minimum normalised cuts, subject to inconsistent advice.