Abstract Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm “DenMune” is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high dimensional datasets relative to several known state of the art clustering algorithms.

中文翻译：

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DenMune：使用相互最近邻的基于密度峰值的聚类

摘要 当簇具有任意形状、不同密度或数据类不平衡且彼此接近时，即使在二维中，许多聚类算法也会失败。提出了一种新颖的聚类算法“DenMune”来应对这一挑战。它基于使用大小为 K 的相互最近邻域来识别密集区域，其中 K 是用户所需的唯一参数，此外还遵守相互最近邻一致性原则。该算法在较大范围的 K 值下都是稳定的。此外，它能够自动检测和去除聚类过程中的噪声以及检测目标簇。相对于几种已知的最先进的聚类算法，它在各种低维和高维数据集上产生了稳健的结果。