Uncertainty in Artificial Intelligence
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Efficient Clustering with Limited Distance Information
Konstantin Voevodski, Maria-Florina Balcan, Heiko Roglin, Shang-Hua Teng, Yu Xia
Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points. In our model we assume that we have access to one versus all queries that given a point s 2 S return the distances between s and all other points. We show that given a natural assumption about the structure of the instance, we can efficiently find an accurate clustering using only O(k) distance queries. We use our algorithm to cluster proteins by sequence similarity. This setting nicely fits our model because we can use a fast sequence database search program to query a sequence against an entire dataset. We conduct an empirical study that shows that even though we query a small fraction of the distances between the points, we produce clusterings that are close to a desired clustering given by manual classification.
Pages: 632-640
PS Link:
PDF Link: /papers/10/p632-voevodski.pdf
AUTHOR = "Konstantin Voevodski and Maria-Florina Balcan and Heiko Roglin and Shang-Hua Teng and Yu Xia",
TITLE = "Efficient Clustering with Limited Distance Information",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "632--640"

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