With the development of Big data technology, data analysis has become increasingly important. Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex datasets. In this paper, we address these problems by proposing a data-driven Bregman divergence parameter optimization clustering algorithm (DBGSA), which combines the Universal Gravitational Algorithm to bring similar points closer in the dataset. We construct a gravitational coefficient equation with a special property that gradually reduces the influence factor as the iteration progresses. Furthermore, we introduce the Bregman divergence generalized power mean information loss minimization to identify cluster centers and build a hyperparameter identification optimization model, which effectively solves the problems of manual adjustment and uncertainty in the improved dataset. Extensive experiments are conducted on four simulated datasets and six real datasets. The results demonstrate that DBGSA significantly improves the accuracy of various clustering algorithms by an average of 63.8\% compared to other similar approaches like enhanced clustering algorithms and improved datasets. Additionally, a three-dimensional grid search was established to compare the effects of different parameter values within threshold conditions, and it was discovered the parameter set provided by our model is optimal. This finding provides strong evidence of the high accuracy and robustness of the algorithm.

提出了一种结合普适引力算法的数据驱动巴格曼散度参数优化聚类算法（DBGSA），通过构建具有特殊属性的引力系数方程，逐渐减少迭代过程中的影响因子，并引入巴格曼散度广义幂均值信息损失最小化来识别聚类中心和构建超参数识别优化模型，有效解决了改进数据集中手动调整和不确定性的问题。对四个模拟数据集和六个真实数据集进行了广泛实验，结果表明DBGSA相对于其他类似方法如改进聚类算法和改进数据集，平均提高了63.8％的各种聚类算法的准确性。此外，建立了一个三维网格搜索来比较阈值条件下不同参数值的效果，并发现我们模型提供的参数集是最优的，这一发现为算法的高准确性和强韧性提供了有力的证据。

DBGSA：一种新的数据自适应Bregman聚类算法