Existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Inspired by adaptive process of granular-ball division and differentiation, we present a novel clustering approach that retains the speed and efficiency of K-means clustering while out-performing time-tested density clustering approaches widely used in industry today. Our simple, robust, adaptive granular-ball clustering method can efficiently recognize clusters with unknown and complex shapes without the use of extra parameters. Moreover, the proposed method provides an efficient, adaptive way to depict the world, and will promote the research and development of adaptive and efficient AI technologies, especially density computing models, and improve the efficiency of many existing clustering methods.

翻译：现有聚类方法基于单一粒度信息（如每个数据的距离和密度）进行聚类。这种最细粒度的处理方法通常效率低下且易受噪声干扰。受粒球划分与分化自适应过程的启发，我们提出了一种新颖的聚类方法，该方法既保留了K-means聚类的速度与效率，又超越了当前工业界广泛应用的经时间检验的密度聚类方法。我们提出的简单、鲁棒、自适应的粒球聚类方法无需额外参数即可高效识别具有未知复杂形状的簇。此外，所提方法提供了一种高效、自适应的世界描述方式，将推动自适应高效人工智能技术（尤其是密度计算模型）的研究与发展，并提升众多现有聚类方法的效率。