Simmered Greedy Optimization for Co-clustering
Sadik Kapadia, Richard Rohwer · 2010
We present a fast yet highly effective stochastic algorithm, Simmered Greedy Optimization (SG(N)) for solving the co-clustering problem: to simultaneously cluster two finite sets by maximizing the mutual information between the clusterings. (Clustering one set by this criterion is a special case.) This is a combinatorial optimization problem of great interest for deriving maximally predictive feature sets. Co-clustering has found applications in many areas, particularly statistical natural language processing and bioinformatics. We report results of tests on a suite of statistical natural language problems, comparing SG(N) with simulated annealing and a publicly available implementation of co-clustering. In all cases we obtain superior results with far less computation using SG(N).