Abstract A challenging issue of clustering in real-word application is to detect clusters with arbitrary shapes and densities in complex data. Many conventional clustering algorithms are capable of detecting non-spherical clusters, but their performance is limited when processing data with complex shapes and multiple density peaks in a cluster without knowing the number of clusters. This paper proposes an adaptive core fusion-based density peak clustering (CFDPC) for detecting clusters in any shape and density adaptively. An initial clustering based on automatic finding of density peaks is proposed first. An adaptive searching approach is then proposed to find core points, and a within-cluster similarity-based core fusion strategy is proposed to obtain the final clustering results. The CFDPC where the number of clusters arises intuitively is simple and efficient. The performance of CFDPC is successfully verified in clustering several benchmark complex datasets with diverse shapes and densities.

中文翻译：

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具有任意形状和密度的复杂数据的基于自适应核心融合的密度峰值聚类

摘要 实际应用中聚类的一个具有挑战性的问题是检测复杂数据中具有任意形状和密度的聚类。许多传统的聚类算法能够检测非球形簇，但是在不知道簇的数量的情况下处理具有复杂形状和多个密度峰值的数据时，它们的性能受到限制。本文提出了一种基于自适应核心融合的密度峰值聚类（CFDPC），用于自适应检测任何形状和密度的聚类。首先提出了基于自动发现密度峰值的初始聚类。然后提出自适应搜索方法来寻找核心点，并提出基于簇内相似性的核心融合策略来获得最终的聚类结果。集群数量直观出现的CFDPC简单高效。CFDPC 的性能在多个具有不同形状和密度的基准复杂数据集的聚类中得到了成功验证。