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41st Annual Symposium on Foundations of Computer Science
On clusterings-good, bad and spectral
Redondo Beach, California
November 12-November 14
ISBN: 0-7695-0850-2
R. Kannan, Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA
S. Vempala, Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA
A. Veta, Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA
We propose a new measure for assessing the quality of a clustering. A simple heuristic is shown to give worst-case guarantees under the new measure. Then we present two results regarding the quality of the clustering found by a popular spectral algorithm. One proffers worst case guarantees whilst the other shows that if there exists a "good" clustering then the spectral algorithm will find one close to it.
Index Terms:
randomised algorithms; computational complexity; pattern clustering; heuristic programming; clustering quality assessment measure; heuristic; worst-case guarantees; spectral algorithm; spectral clustering; randomized algorithm; polynomial time algorithms
Citation:
R. Kannan, S. Vempala, A. Veta, "On clusterings-good, bad and spectral," focs, pp.367, 41st Annual Symposium on Foundations of Computer Science, 2000
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