Owing to the tremendous diversity and complexity of data in today’s world, some new insights for clustering on data are often desired by developing an alternative to the existing clustering approaches. In this paper, based on the new concepts of the Bayesian transmission system and its transmission learning, a label manifold-based transmission learning machine for clustering (LMTLMC) is accordingly developed. As the first attempt to explain the clustering behavior in a lifelike way, LMTLMC is well justified by revealing the natural parallel between its gradient-based optimization process and the statistical diffusion in statistical physics through the modified Fick’s diffusion law for clustering. Practically, LMTLMC is distinctive in its easy implementation in terms of its global analytical solution, its easy parameter settings and its stable and efficient clustering results. Extensive experiments on synthetic datasets and real datasets demonstrate the promising performance and superiority of LMTLMC for clustering tasks, in contrast to the existing clustering algorithms.

由于当今世界数据的巨大多样性和复杂性，通常需要通过开发一种替代现有聚类方法的方法来寻求有关数据聚类的新见解。在本文中，基于贝叶斯传输系统的新的概念和它的传输学习，一个升阿贝尔米anifold基于吨ransmission升收益米achine为Ç相应地开发了光泽（LMTLMC）。作为首次以逼真的方式解释聚类行为的尝试，通过修改后的Fick聚类扩散定律，揭示了基于梯度的优化过程与统计物理学中的统计扩散之间的自然相似性，从而证明了LMTLMC的合理性。实际上，LMTLMC的独特之处在于其易于执行的全局分析解决方案，简单的参数设置以及稳定高效的聚类结果。在合成数据集和真实数据集上进行的大量实验表明，与现有的聚类算法相比，LMTLMC在聚类任务方面具有令人鼓舞的性能和优越性。