Handling clustering problems are important in data statistics, pattern recognition and image processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to solve clustering problems. However, the mean-shift algorithm is restricted by its huge computational resource cost. In previous research[10], we proposed a novel GPU-accelerated Faster Mean-shift algorithm, which greatly speed up the cosine-embedding clustering problem. In this study, we extend and improve the previous algorithm to handle Euclidean distance metrics. Different from conventional GPU-based mean-shift algorithms, our algorithm adopts novel Seed Selection & Early Stopping approaches, which greatly increase computing speed and reduce GPU memory consumption. In the simulation testing, when processing a 200K points clustering problem, our algorithm achieved around 3 times speedup compared to the state-of-the-art GPU-based mean-shift algorithms with optimized GPU memory consumption. Moreover, in this study, we implemented a plug-and-play model for faster mean-shift algorithm, which can be easily deployed. (Plug-and-play model is available: https://github.com/masqm/Faster-Mean-Shift-Euc)

翻译：在数据统计、模式识别和图像处理中,处理群集问题很重要。 平均变换算法(一种共同的不受监督的算法)被广泛用来解决群集问题。 但是,平均变换算法受到其庞大计算资源成本的限制。 在以前的研究[10]中,我们建议采用新的GPU加速快速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速算法,这大大加快了计算速度。在这个研究中,我们扩展并改进了先前的算法,以处理欧洲超速超速超速超速超速超速。不同于传统的基于GPU的普通平均变换算法,我们的算法采用了新颖的种子选择和早期停用法方法,这大大提高了计算速度并减少了GPU的记忆消耗量。在模拟测试中,当处理一个200K点集时,我们的算法比基于最先进的GPU的超速超速超速超速超速超速超速超速超速的算法。 此外,我们采用了一种超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速超速的超速算法。