On spectral clustering: Analysis and an algorithm

Andrew Y. Ng, Michael I. Jordan, Yair Weiss

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

4169 Scopus citations

Abstract

Despite many empirical successes of spectral clustering methods- Algorithms that cluster points using eigenvectors of matrices derived from the data-there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly different ways. Second, many of these algorithms have 110 proof that they will actually compute a reasonable clustering. T11 this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results 011 a number of challenging clustering problems.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001
PublisherNeural information processing systems foundation
ISBN (Print)0262042088, 9780262042086
StatePublished - 2002
Event15th Annual Neural Information Processing Systems Conference, NIPS 2001 - Vancouver, BC, Canada
Duration: 3 Dec 20018 Dec 2001

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference15th Annual Neural Information Processing Systems Conference, NIPS 2001
Country/TerritoryCanada
CityVancouver, BC
Period3/12/018/12/01

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