Efficient and robust data clustering remains a challenging task in the field of data analysis. Recent efforts have explored the integration of granular-ball (GB) computing with clustering algorithms to address this challenge, yielding promising results. However, existing methods for generating GBs often rely on single indicators to measure GB quality and employ threshold-based or greedy strategies, potentially leading to GBs that do not accurately capture the underlying data distribution. To address these limitations, this article introduces a novel GB generation method. The originality of this method lies in leveraging the principle of justifiable granularity to measure the quality of a GB for clustering tasks. To be precise, we define the coverage and specificity of a GB and introduce a comprehensive measure for assessing GB quality. Utilizing this quality measure, the method incorporates a binary tree pruning-based strategy and an anomaly detection method to determine the best combination of sub-GBs for each GB and identify abnormal GBs, respectively. Compared to previous GB generation methods, the new method maximizes the overall quality of generated GBs while ensuring alignment with the data distribution, thereby enhancing the rationality of the generated GBs. Experimental results obtained from both synthetic and publicly available datasets underscore the effectiveness of the proposed GB generation method, showcasing improvements in clustering accuracy and normalized mutual information.

翻译：在数据分析领域，高效稳健的数据聚类仍是一项具有挑战性的任务。近期研究尝试将粒球计算与聚类算法相结合以应对这一挑战，并取得了令人鼓舞的成果。然而，现有粒球生成方法通常依赖单一指标衡量粒球质量，并采用阈值或贪心策略，可能导致生成的粒球无法准确反映潜在的数据分布。为克服这些局限，本文提出一种新型粒球生成方法。该方法创新性地运用可解释粒度原理来衡量聚类任务中粒球的质量。具体而言，我们定义粒球的覆盖度与特异性，并引入综合指标评估粒球质量。基于该质量指标，方法采用二叉树剪枝策略与异常检测技术，分别确定每个粒球的最优子粒球组合并识别异常粒球。相较于既有粒球生成方法，本方法在确保与数据分布一致性的前提下最大化所生成粒球的整体质量，从而提升粒球的合理性。在合成数据集与公开数据集上开展的实验结果表明，所提出的粒球生成方法在聚类精度与归一化互信息指标上均取得显著提升，验证了其有效性。