Stratifying personalized treatment for patients has been one of the main challenges for modern medicine. To solve this problem, various clustering algorithms have been proposed for patient stratification in both quantification and biological ways meaningfully. However, most of the existing clustering algorithms still suffer from many realistic algorithm limitations such as low diagnostic ability and bad generalization. Therefore, to address those restrictions, we propose a novel multiobjective spectral clustering algorithm based on decomposition. A population that consists of distance weight and two other indispensable parameters of the spectral clustering is optimized by the proposed algorithm. Two cluster validity indices are proposed to capture the characteristics of different datasets. To validate the effectiveness and efficiency of the proposed algorithm, we benchmark it on thirty-five real patient stratification datasets and six real-world medical datasets across thousands of comparisons with fifteen algorithms, including ten effective clustering methods and five state-of-the-art multiobjective algorithms. The experimental results indicate that the proposed algorithm performs better than other compared algorithms with high clustering ability for patient stratification. Moreover, extensive analysis of time complexity and parameters are performed to prove the robustness of the proposed algorithm from different perspectives.

对患者进行个性化治疗已成为现代医学的主要挑战之一。为了解决该问题，已经提出了有意义的量化和生物学方式的各种聚类算法用于患者分层。但是，大多数现有的聚类算法仍然遭受许多现实的算法限制，例如诊断能力低和泛化能力差。因此，为了解决这些限制，我们提出了一种基于分解的新颖的多目标谱聚类算法。该算法优化了由距离权重和频谱聚类的两个其他必不可少的参数组成的总体。提出了两个聚类有效性指标来捕获不同数据集的特征。为了验证所提出算法的有效性和效率，我们在15种算法的35个真实患者分层数据集和6个真实世界医学数据集上进行了基准测试，并通过15种算法进行了数千次比较，其中包括10种有效的聚类方法和5种最新状态。艺术多目标算法。实验结果表明，所提出的算法比其他同类算法具有更好的聚类能力，能够更好地进行患者分层。此外，对时间复杂度和参数进行了广泛的分析，以从不同角度证明所提出算法的鲁棒性。包括十种有效的聚类方法和五种最新的多目标算法。实验结果表明，所提出的算法比其他同类算法具有更好的聚类能力，能够更好地进行患者分层。此外，对时间复杂度和参数进行了广泛的分析，以从不同角度证明所提出算法的鲁棒性。包括十种有效的聚类方法和五种最新的多目标算法。实验结果表明，所提出的算法比其他同类算法具有更好的聚类能力，能够更好地进行患者分层。此外，对时间复杂度和参数进行了广泛的分析，以从不同角度证明所提出算法的鲁棒性。