Spectral clustering with inconsistent advice

Torn Coleman, James Saunderson, Anthony Wirth

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

40 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Machine Learning
Number of pages8
Publication statusPublished - 2008
Externally publishedYes
EventInternational Conference on Machine Learning 2008 - Helsinki, Finland
Duration: 5 Jul 20089 Jul 2008
Conference number: 25th


ConferenceInternational Conference on Machine Learning 2008
Abbreviated titleICML 2008
Internet address

Cite this