GBCT: Efficient and Adaptive Clustering via Granular-Ball Computing for Complex Data

Abstract

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 the 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 via granular-ball computing. First, clustering algorithm based on granular-ball (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 nonspherical datasets than other traditional clustering methods. The completely new coarse granularity representation method of GBCT and cluster formation mode can also be used to improve other traditional methods. All codes can be available at https://github.com/wylbdthxbw/GBC.