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We study the problem of efficiently clustering protein sequences in a limitedinformation setting. We assume that we do not know the distances between thesequences in advance, and must query them during the execution of thealgorithm. Our goal is to find an accurate clustering using few queries. Wemodel the problem as a point set $S$ with an unknown metric $d$ on $S$, andassume that we have access to \emph{one versus all} distance queries that givena point $s \in S$ return the distances between $s$ and all other points. Ourone versus all query represents an efficient sequence database search programsuch as BLAST, which compares an input sequence to an entire data set. Given anatural assumption about the approximation stability of the \emph{min-sum}objective function for clustering, we design a provably accurate clusteringalgorithm that uses few one versus all queries. In our empirical study we showthat our method compares favorably to well-established clustering algorithmswhen we compare computationally derived clusterings to gold-standard manualclassifications.

Clustering Protein Sequences Given the Approximation Stability of the Min-Sum Objective Function