Abstract Clustering is among the tools for exploring, analyzing, and deriving information from data. In the case of large data sets, the real burden to the application of clustering algorithms can be their complexity and demand of control parameters. We present a new fast nonparametric clustering algorithm, UNIC, to address these challenges. To identify clusters, the algorithm evaluates the distances between selected points and other points in the set. While assessing these distances, it employs methods of robust statistics to identify the cluster borders. The performance of the proposed algorithm is assessed in an experimental study and compared with several existing clustering methods over a variety of benchmark data sets.

中文翻译：

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UNIC：快速非参数聚类

摘要 聚类是探索、分析和从数据中获取信息的工具之一。在大数据集的情况下，聚类算法应用的真正负担可能是它们的复杂性和控制参数的需求。我们提出了一种新的快速非参数聚类算法 UNIC，以应对这些挑战。为了识别集群，该算法评估所选点与集合中其他点之间的距离。在评估这些距离时，它采用稳健的统计方法来识别集群边界。在实验研究中评估了所提出算法的性能，并在各种基准数据集上与几种现有的聚类方法进行了比较。