Single‐linkage clustering of dynamic data

Abstract

SummaryThe surge in data sizes in fluid processing applications necessitates partitioning the data into clusters and studying their representatives instead of studying each voxel data point. In addition, the dynamic nature of these data poses further challenges. Under such circumstances, it becomes essential to develop an approach that can handle the delta data with minimal updates to the underlying data structure, without processing the complete data from scratch on every update. However, this poses synchronization challenges in parallelization. In this article, we propose SLCoDD (single‐linkage clustering of dynamic data), a geometric distance based dynamic clustering and its multi‐core parallelization using OpenMP. To improve efficiency, SLCoDD exploits geometric properties of the bounding squares. We illustrate trade‐offs in various ways of performing point additions to clusters, point deletions, and their batched versions. Using a suite of large inputs, we demonstrate the effectiveness of SLCoDD. SLCoDD's fully dynamic version achieves a substantial geomean speedup of over the static parallel version and of over the dynamic sequential version.