ArticleNature communications2022
Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 77 citations in OpenAlex.
- LiDAR-derived dataset of extracted trees and shrubs from AHN4 airborne laser scanning in the Oostvaardersplassen wetland, the Netherlands.Data in brief · 2026Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- Assessing summer thermal environment in humid cities: Local climate zone perspective.iScience · 2025Article
- DKCDC: A clustering algorithm focusing on genuine boundary search for regional division.PloS one · 2025Article
- FLASC: a flare-sensitive clustering algorithm.PeerJ. Computer science · 2025Article
- Drone-Based Localization of Hazardous Chemicals by Passive Smart Dust.Sensors (Basel, Switzerland) · 2024Article
- Identifying clinically meaningful subgroups following open reduction and internal fixation for proximal humerus fractures: a risk stratification analysis for mortality and 30-day complications using machine learning.JSES international · 2024Article
- Dual hybrid frameworks combining graph convolutional network with decoding for covering location problem.iScience · 2024Article
- Grid-Based Clustering Using Boundary Detection.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. It is widely used in computer science, bioscience, geoscience, and economics. Although the state-of-the-art partition-based and connectivity-based clustering methods have been developed, weak connectivity and heterogeneous density in data impede their effectiveness. In this work, we propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks.
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