kFuse: A novel density based agglomerative clustering

• URL: http://arxiv.org/abs/2505.05748v1
• Date: Fri, 09 May 2025 03:11:04 GMT
• Title: kFuse: A novel density based agglomerative clustering
• Authors: Huan Yan, Junjie Hu,
• Abstract summary: This paper introduces a density-based agglomerative clustering method, termed kFuse.<n> kFuse comprises four key components: (1) sub-cluster partitioning based on natural neighbors; (2) determination of boundary connectivity between sub-clusters through the computation of adjacent samples and shortest distances; and (3) assessment of density similarity between sub-clusters via the calculation of mean density and variance.<n> Experimental results on both synthetic and real-world datasets validate the effectiveness of kFuse.
• Score: 9.061140802902514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agglomerative clustering has emerged as a vital tool in data analysis due to its intuitive and flexible characteristics. However, existing agglomerative clustering methods often involve additional parameters for sub-cluster partitioning and inter-cluster similarity assessment. This necessitates different parameter settings across various datasets, which is undoubtedly challenging in the absence of prior knowledge. Moreover, existing agglomerative clustering techniques are constrained by the calculation method of connection distance, leading to unstable clustering results. To address these issues, this paper introduces a novel density-based agglomerative clustering method, termed kFuse. kFuse comprises four key components: (1) sub-cluster partitioning based on natural neighbors; (2) determination of boundary connectivity between sub-clusters through the computation of adjacent samples and shortest distances; (3) assessment of density similarity between sub-clusters via the calculation of mean density and variance; and (4) establishment of merging rules between sub-clusters based on boundary connectivity and density similarity. kFuse requires the specification of the number of clusters only at the final merging stage. Additionally, by comprehensively considering adjacent samples, distances, and densities among different sub-clusters, kFuse significantly enhances accuracy during the merging phase, thereby greatly improving its identification capability. Experimental results on both synthetic and real-world datasets validate the effectiveness of kFuse.

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