Data Clustering Using Variants of Rapid Centroid Estimation

Prior work suggests that particle swarm clustering (PSC) can be a powerful tool for solving clustering problems. This paper reviews parts of the PSC algorithm, and shows how and why a new class of algorithms is proposed in an attempt to improve the efficiency and repeatability of PSC. This new implementation is referred to as rapid centroid estimation (RCE). RCE simplifies the update rules of PSC, and greatly reduces computational complexity by enhancing the efficiency of the particle trajectories. On benchmark evaluations with an artificial dataset that has 80 dimensions and a volume of 5000, the RCE variants have iteration times of less than 0.1 s, which compares to iteration times of 2 s for PSC and modified PSC (mPSC). On UC Irvine (UCI) machine learning benchmark datasets, the RCE variants are much faster than PSC and mPSC, and produce clusters with higher purity and greatly improved optimization speeds. For example, the RCE variants are more than 100 times faster than PSC and mPSC on the UCI breast cancer dataset. It can be concluded that the RCE variants are leaner and faster than PSC and mPSC, and that the new optimization strategies also improve clustering quality and repeatability.