Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios

2025-05-14 · Siyi Wang, Alexandre Leblanc, Paul D. McNicholas

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.

📄 PDF Abstract BibTeX arXiv:2505.09516

Code (1)

lytgysrn/dlcc 공식 구현

Tasks

Clustering

Learning Pixel-wise Continuous Depth Representation via Clustering for Depth Completion

2024-02-21 · Chen Shenglun, Zhang Hong, Ma XinZhu, Wang Zhihui 외

Depth completion is a long-standing challenge in computer vision, where classification-based methods have made tremendous progress in recent years. However, most existing classification-based methods rely on pre-defined …

Clustering Depth Completion

Automated Steel Bar Counting and Center Localization with Convolutional Neural Networks

2019-06-03 · Zhun Fan, Jiewei Lu, Benzhang Qiu, Tao Jiang 외

Automated steel bar counting and center localization plays an important role in the factory automation of steel bars. Traditional methods only focus on steel bar counting and their performances are often limited by compl…

Clustering

Federated Learning for Short Text Clustering

2023-11-23 · Mengling Hu, Chaochao Chen, Weiming Liu, Xinting Liao 외

Short text clustering has been popularly studied for its significance in mining valuable insights from many short texts. In this paper, we focus on the federated short text clustering (FSTC) problem, i.e., clustering sho…

Clustering Federated Learning Short Text Clustering Text Clustering

COT: Unsupervised Domain Adaptation With Clustering and Optimal Transport

2023-01-01 · CVPR 2023 1 · Yang Liu, Zhipeng Zhou, Baigui Sun

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge transfer, aligning the distribution between …

Clustering Domain Adaptation Transfer Learning Unsupervised Domain Adaptation

Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

2026-05-27 · Lei Zhang, Fubo Sun, Haipeng Yang, Zhong Guan 외 arxiv

Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have improved clustering via structural and att…

Contrastive Learning Graph Clustering