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

作     者：Xia, Shuyin Shi, Bolun Wang, Yifan Xie, Jiang Wang, Guoyin Gao, Xinbo 

作者机构：Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Key Lab Cyberspace Big Data Intelligent Secur Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Dept Comp Sci Chongqing 400065 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS》 (IEEE Trans. Neural Networks Learn. Sys.)

年 卷 期：2025年第PP卷

页      面：PP页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [62222601, 62221005, 62176033] Key Cooperation Project of Chongqing Municipal Education Commission [HZ2021008] Natural Science Foundation of Chongqing [cstc2019jcyj-cxttX0002, CSTB2023NSCQ-JQX0034] 

主　　题：Clustering algorithms Partitioning algorithms Computational modeling Robustness Noise Approximation algorithms Mathematical models Clustering methods Telecommunications Shape Clustering granular computing granular-ball multigranularity 

摘      要：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 methodsbad 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.