GBCT: An Efficient and Adaptive Granular-Ball Clustering Algorithm for Complex Data
This addresses clustering challenges in complex data analysis, offering a novel approach that enhances efficiency and robustness, though it appears incremental as it builds on granular computing concepts.
The authors tackled the inefficiency and poor robustness of traditional clustering algorithms by proposing GBCT, a granular-ball clustering algorithm that uses coarse-grained granular-balls to represent data, resulting in improved efficiency, robustness, and better performance on non-spherical datasets compared to traditional methods.
Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in human brain, resulting in those methods' bad performance in efficiency, generalization ability and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering (GBCT) via granular-ball computing. Firstly, GBCT generates a smaller number of granular-balls to represent the original data, and forms clusters according to the relationship between granular-balls, instead of the traditional point relationship. At the same time, its coarse-grained characteristics are not susceptible to noise, and the algorithm is efficient and robust; besides, as granular-balls can fit various complex data, GBCT performs much better in non-spherical data sets than other traditional clustering methods. The completely new coarse granularity representation method of GBCT and cluster formation mode can also used to improve other traditional methods.