Tree-Cut Algorithms for Generating Cluster Ensembles
From: Uncovering Many Views of Biological Networks Using Ensembles of Near-Optimal Partitions
* Equally contributed to this work.
Densely interacting regions of biological networks often correspond to
functional modules such as protein complexes. Most algorithms proposed to
uncover modules, however, produce one clustering that only reveals a single
view of how the cell is organized. We describe two new methods to find
ensembles of provably near-optimal modularity partitions that lie within a
heuristically constrained search space. We also show how to count the number of
solutions in this space that lie within a bounded modularity range. We apply
our algorithms to a protein interaction network for S.cerevisiae
and show how fine-grained differences between near-optimal partitions can be
used to define robust communities. We also propose a technique to find
structurally diverse near-optimal solutions and show that these different
partitions are enriched for different biological functions. Our results
indicate that near-optimal solutions represent alternative and complementary
views of the network's structure.
Overview of the Modu-Cut algorithm.
In Proceedings of the 1st MultiClust KDD Workshop on Discovering, Summarizing, and Using Multiple Clusterings, 2010.
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Last modified: June 24, 2013