Spectral clustering is a hot topic and many spectral clustering algorithms have been proposed. These algorithms usually solve the discrete cluster indicator matrix by relaxing the original problems, obtaining the continuous solution and finally obtaining a discrete solution that is close to the continuous solution. However, such methods often result in a non-optimal solution to the original problem since the different steps solve different problems. In this paper, we propose a novel spectral clustering method, named as Enhanced Balanced Min Cut (EBMC). In the new method, a new normalized cut model is proposed, in which a set of balance parameters are learned to capture the differences among different clusters. An iterative method with proved convergence is used to effectively solve the new model without eigendecomposition. Theoretical analysis reveals the connection between EBMC and the classical normalized cut. Extensive experimental results show the effectiveness and efficiency of our approach in comparison with the state-of-the-art methods.

中文翻译：

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增强平衡最小切割

谱聚类是一个热门话题，已经提出了许多谱聚类算法。这些算法通常通过放宽原问题，得到连续解，最终得到接近连续解的离散解来求解离散聚类指标矩阵。但是，由于不同的步骤解决不同的问题，因此此类方法通常会导致原始问题的非最佳解决方案。在本文中，我们提出了一种新的谱聚类方法，称为增强平衡最小切割（EBMC）。在新方法中，提出了一种新的归一化切割模型，其中学习了一组平衡参数来捕捉不同集群之间的差异。使用具有证明收敛性的迭代方法有效地求解没有特征分解的新模型。理论分析揭示了 EBMC 与经典归一化切割之间的联系。大量的实验结果表明，与最先进的方法相比，我们的方法的有效性和效率。