An algorithm is developed for solving clustering problems with the similarity measure defined using the $ L_1 $ and $ L_\infty $ norms. It is based on an incremental approach and applies nonsmooth optimization methods to find cluster centers. Computational results on 12 data sets are reported and the proposed algorithm is compared with the $ X $-means algorithm.

中文翻译：

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用于聚类的增量非光滑优化算法。 \ begin {document} $ L_1 $ \ end {document} 和 \ begin {document} $ L_ \ infty $ \ end {document} 规范

开发了一种算法，用于解决使用$ L_1 $和$ L_ \ infty $范数定义的相似性度量的聚类问题。它基于增量方法，并应用非平滑优化方法来查找聚类中心。报告了12个数据集的计算结果，并将所提出的算法与$ X $ -means算法进行了比较。