TY - GEN
T1 - A randomized algorithm for pairwise clustering
AU - Gdalyahu, Yoram
AU - Weinshall, Daphna
AU - Werman, Michael
PY - 1999
Y1 - 1999
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=72749116899&partnerID=8YFLogxK
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AN - SCOPUS:72749116899
SN - 0262112450
SN - 9780262112451
T3 - Advances in Neural Information Processing Systems
SP - 424
EP - 430
BT - Advances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998
PB - Neural information processing systems foundation
T2 - 12th Annual Conference on Neural Information Processing Systems, NIPS 1998
Y2 - 30 November 1998 through 5 December 1998
ER -