Parallel Clustering with Resolution Variation
We introduce a novel approach for parallel data clustering with resolution variation. Conventional graph clustering is typically governed by a function defined over all possible cluster configurations but at a fixed value of the resolution hyperparameter denoted as $\gamma$. Such clustering suffers from issues related to the so-called resolution limit or requires resolution tuning. This has been changed by recent theories and algorithms for graph clustering with resolution variation. The requirement for specifying or tuning the $\gamma$ -hyperparameter is effectively removed, and the clustering function becomes or transforms to a functional form with $\gamma$ as an internal resolution variable. We address a standing and significant challenge in parallel clustering with resolution variation. We identify and remove the key bottlenecks in search operations confined to a specific $\gamma$ value, and reduce and minimize redundant search operations at different $\gamma$ values. We show impressive performance achieved with our parallel approach on real-world datasets.
