A Parallel Local Search Algorithm for Clustering Large Biological Networks

Clustering is an effective technique that can be used to analyze and extract useful information from large biological networks. Popular clustering solutions often require user input for several algorithm options that can seem very arbitrary without experimentation. These algorithms can provide good results in a reasonable time period but they are not above improvements. We present a local search based clustering algorithm free of such required input that can be used to improve the cluster quality of a set of given clusters taken from any existing algorithm or clusters produced via any arbitrary assignment. We implement this local search using a modern GPU based approach to allow for efficient runtime. The proposed algorithm shows promising results for improving the quality of clusters. With already high quality input clusters we can achieve cluster rating improvements upto to 33%.