Active Clustering of Biological Sequences

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 ∈ 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. Our algorithm uses an active selection strategy to choose a small set of points that we call landmarks, and considers only the distances between landmarks and other points to produce a clustering. We use our procedure 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 data set. We conduct an empirical study that shows that even though we query a small fraction of the distances between the point...

• Type: Journal
• Year: 2012
• Where: JMLR
• Authors: Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia
• Keywords: Database Search Program, JMLR 2012, Natural Assumption, Programming Languages, Sequence Database Search