Sublinear time approximate clustering
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Approximating min-sum k-clustering in metric spaces
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Local Search Heuristics for k-Median and Facility Location Problems
SIAM Journal on Computing
Sublinear-time approximation algorithms for clustering via random sampling
Random Structures & Algorithms - Proceedings from the 12th International Conference “Random Structures and Algorithms”, August1-5, 2005, Poznan, Poland
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Approximate clustering without the approximation
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
A uniqueness theorem for clustering
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Active clustering of biological sequences
The Journal of Machine Learning Research
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We study the problem of efficiently clustering protein sequences in a limited information setting. We assume that we do not know the distances between the sequences in advance, and must query them during the execution of the algorithm. Our goal is to find an accurate clustering using few queries. We model the problem as a point set S with an unknown metric d on S, and assume that we have access to one versus all distance queries that given a point s ∈ S return the distances between s and all other points. Our one versus all query represents an efficient sequence database search program such as BLAST, which compares an input sequence to an entire data set. Given a natural assumption about the approximation stability of the min-sum objective function for clustering, we design a provably accurate clustering algorithm that uses few one versus all queries. In our empirical study we show that our method compares favorably to well-established clustering algorithms when we compare computationally derived clusterings to gold-standard manual classifications.