A randomized algorithm for pairwise clustering

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

15 Scopus citations


We present a stochastic clustering algorithm based on pairwise similarity of datapoints. Our method extends existing deterministic methods, including agglomerative algorithms, min-cut graph algorithms, and connected components. Thus it provides a common framework for all these methods. Our graph-based method differs from existing stochastic methods which are based on analogy to physical systems. The stochastic nature of our method makes it more robust against noise, including accidental edges and small spurious clusters. We demonstrate the superiority of our algorithm using an example with 3 spiraling bands and a lot of noise.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998
PublisherNeural information processing systems foundation
Number of pages7
ISBN (Print)0262112450, 9780262112451
StatePublished - 1999
Event12th Annual Conference on Neural Information Processing Systems, NIPS 1998 - Denver, CO, United States
Duration: 30 Nov 19985 Dec 1998

Publication series

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


Conference12th Annual Conference on Neural Information Processing Systems, NIPS 1998
Country/TerritoryUnited States
CityDenver, CO


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