  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.
