Abstract Recently, we have proposed a novel physically-inspired method, called the Nearest Descent (ND), which plays the role of organizing all the samples into an effective Graph, called the in-tree. Due to its effective characteristics, this in-tree proves very suitable for data clustering. Nevertheless, this in-tree-based clustering still has some non-trivial limitations in terms of robustness, capability, etc. In this study, we first propose a distance-ensemble-based framework for the in-tree-based clustering, which proves a very convenient way to overcome the robustness limitation in our previous in-tree-based clustering. To enhance the capability of the in-tree-based clustering in handling extremely linearly-inseparable clusters, we kernelize the proposed ensemble-based clustering via the so-called kernel trick. As a result, the improved in-tree-based clustering method achieves high robustness and accuracy on diverse challenging synthetic and real-world datasets, showing a certain degree of practical value.

中文翻译：

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通过距离集成和内核化增强基于树的聚类

摘要 最近，我们提出了一种新的物理启发方法，称为最近下降（ND），它的作用是将所有样本组织成一个有效的图，称为 in-tree。由于其有效的特性，这种 in-tree 证明非常适合数据聚类。尽管如此，这种基于树内的聚类在鲁棒性、能力等方面仍然存在一些非平凡的局限性。 在这项研究中，我们首先提出了一个基于距离集成的基于树内聚类的框架，这证明一种非常方便的方法来克服我们之前的基于树的聚类中的鲁棒性限制。为了增强基于 in-tree 的聚类处理极其线性不可分的聚类的能力，我们通过所谓的内核技巧对所提出的基于集成的聚类进行内核化。因此，