Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity

Dehua Peng, Zhipeng Gui (), Dehe Wang, Yuncheng Ma, Zichen Huang, Yu Zhou and Huayi Wu
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Dehua Peng: Wuhan University
Zhipeng Gui: Wuhan University
Dehe Wang: Wuhan University
Yuncheng Ma: Wuhan University
Zichen Huang: Wuhan University
Yu Zhou: Wuhan University
Huayi Wu: Wuhan University

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-33136-9

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