基于深度的局部中心聚类，可处理复杂形状数据聚类
Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios
- 用局部数据深度识别中心点，适应多模态数据
- 能发现非凸、不规则形状的聚类结构
- 适合处理传统方法难以应对的复杂场景
聚类分析在众多科学与工程领域中具有关键作用。尽管过去几十年提出了大量聚类方法，但每种方法通常针对特定场景设计，在实际应用中仍存在局限性。本文提出一种基于深度的局部中心聚类（DLCC）方法。该方法利用数据深度在多元空间中实现样本点的中心向外排序。然而，传统数据深度难以捕捉数据的多模态特性，这对聚类至关重要。为克服此问题，DLCC采用基于数据子集的局部数据深度，由此可识别局部中心并发现不同形状的聚类。此外，我们提出一种基于密度的内部评估指标，用于衡量在非凸聚类上的性能。总体而言，DLCC是一种灵活的聚类方法，能够克服传统方法的部分局限性，从而提升在多种应用场景下的数据分析能力。
原文摘要 · Abstract (English)
Cluster analysis, or clustering, plays a crucial role across numerous scientific and engineering domains. Despite the wealth of clustering methods proposed over the past decades, each method is typically designed for specific scenarios and presents certain limitations in practical applications. In this paper, we propose depth-based local center clustering (DLCC). This novel method makes use of data depth, which is known to produce a center-outward ordering of sample points in a multivariate space. However, data depth typically fails to capture the multimodal characteristics of {data}, something of the utmost importance in the context of clustering. To overcome this, DLCC makes use of a local version of data depth that is based on subsets of {data}. From this, local centers can be identified as well as clusters of varying shapes. Furthermore, we propose a new internal metric based on density-based clustering to evaluate clustering performance on {non-convex clusters}. Overall, DLCC is a flexible clustering approach that seems to overcome some limitations of traditional clustering methods, thereby enhancing data analysis capabilities across a wide range of application scenarios.
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