A Novel Mean-Shift Algorithm for Data Clustering

We propose a novel Mean-Shift method for data clustering, called Robust (RMS). A new update equation point iterates is proposed, mixing the ones of standard (MS) and Blurring (BMS). Despite its simplicity, proposed has not been studied so far. RMS can be set up in both kernel-based nearest-neighbor (NN)-based fashion. Since rule closer to BMS, convergence conjectured based on Chen’s BMS theorem. Experimental results synthetic real datasets show that several cases outperforms MS clustering task. In addition, exhibits larger attraction basins than identical parametrization; consequently, kernel variant requires lower aperture function, NN number nearest neighbors compared or achieve optimal results. version does need specify threshold stop iterations, contrarily NN-BMS algorithm.