GBC: An Efficient and Adaptive Clustering Algorithm Based on Granular-Ball
This addresses the need for more efficient and robust clustering algorithms in data analysis applications, though it appears to be an incremental improvement over existing methods.
The paper tackles the problem of inefficient and noise-sensitive clustering methods by proposing a granular-ball clustering algorithm that recognizes clusters with unknown complex shapes without extra parameters, achieving speed comparable to K-means while outperforming density-based methods.
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.