A clustering algorithm based on grids for core data and adjacency relationships for edge data
Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such as parameter sensitivity, poor adaptability to density variations, and misclassification of edge data. To address these issues, existing research prim...
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| Vydáno v: | Scientific reports Ročník 15; číslo 1; s. 18390 - 36 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Nature Publishing Group UK
26.05.2025
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such as parameter sensitivity, poor adaptability to density variations, and misclassification of edge data. To address these issues, existing research primarily focuses on three directions: (1) optimizing the adaptive selection of grid parameters, which struggles to handle variations in cluster density; (2) improving grid division methods (e.g., multi-granularity or dynamic grids), which have limited effectiveness on complex-shaped data; and (3) integrating other clustering techniques, which enhances accuracy but increases algorithmic complexity. This paper proposes a novel improved grid-based clustering algorithm that determines core grids based on data distribution uniformity rather than absolute density and introduces a clustering strategy for non-core grids based on adjacency relationships. This approach effectively identifies clusters with different densities and reduces dependency on initial parameters (density threshold
R
and grid partition parameters
M
). The proposed algorithm integrates grid clustering, partitioning-based clustering, and grid splitting techniques. It employs a regional processing strategy—applying grid clustering to cluster core regions while combining grid and Partitioning techniques for edge regions—to enhance accuracy while maintaining efficiency. Experimental results demonstrate that the proposed algorithm outperforms six other benchmark algorithms on datasets with complex shapes and uneven densities, achieving a balance between efficiency and accuracy. |
|---|---|
| AbstractList | Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such as parameter sensitivity, poor adaptability to density variations, and misclassification of edge data. To address these issues, existing research primarily focuses on three directions: (1) optimizing the adaptive selection of grid parameters, which struggles to handle variations in cluster density; (2) improving grid division methods (e.g., multi-granularity or dynamic grids), which have limited effectiveness on complex-shaped data; and (3) integrating other clustering techniques, which enhances accuracy but increases algorithmic complexity. This paper proposes a novel improved grid-based clustering algorithm that determines core grids based on data distribution uniformity rather than absolute density and introduces a clustering strategy for non-core grids based on adjacency relationships. This approach effectively identifies clusters with different densities and reduces dependency on initial parameters (density threshold R and grid partition parameters M). The proposed algorithm integrates grid clustering, partitioning-based clustering, and grid splitting techniques. It employs a regional processing strategy-applying grid clustering to cluster core regions while combining grid and Partitioning techniques for edge regions-to enhance accuracy while maintaining efficiency. Experimental results demonstrate that the proposed algorithm outperforms six other benchmark algorithms on datasets with complex shapes and uneven densities, achieving a balance between efficiency and accuracy.Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such as parameter sensitivity, poor adaptability to density variations, and misclassification of edge data. To address these issues, existing research primarily focuses on three directions: (1) optimizing the adaptive selection of grid parameters, which struggles to handle variations in cluster density; (2) improving grid division methods (e.g., multi-granularity or dynamic grids), which have limited effectiveness on complex-shaped data; and (3) integrating other clustering techniques, which enhances accuracy but increases algorithmic complexity. This paper proposes a novel improved grid-based clustering algorithm that determines core grids based on data distribution uniformity rather than absolute density and introduces a clustering strategy for non-core grids based on adjacency relationships. This approach effectively identifies clusters with different densities and reduces dependency on initial parameters (density threshold R and grid partition parameters M). The proposed algorithm integrates grid clustering, partitioning-based clustering, and grid splitting techniques. It employs a regional processing strategy-applying grid clustering to cluster core regions while combining grid and Partitioning techniques for edge regions-to enhance accuracy while maintaining efficiency. Experimental results demonstrate that the proposed algorithm outperforms six other benchmark algorithms on datasets with complex shapes and uneven densities, achieving a balance between efficiency and accuracy. Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such as parameter sensitivity, poor adaptability to density variations, and misclassification of edge data. To address these issues, existing research primarily focuses on three directions: (1) optimizing the adaptive selection of grid parameters, which struggles to handle variations in cluster density; (2) improving grid division methods (e.g., multi-granularity or dynamic grids), which have limited effectiveness on complex-shaped data; and (3) integrating other clustering techniques, which enhances accuracy but increases algorithmic complexity. This paper proposes a novel improved grid-based clustering algorithm that determines core grids based on data distribution uniformity rather than absolute density and introduces a clustering strategy for non-core grids based on adjacency relationships. This approach effectively identifies clusters with different densities and reduces dependency on initial parameters (density threshold R and grid partition parameters M ). The proposed algorithm integrates grid clustering, partitioning-based clustering, and grid splitting techniques. It employs a regional processing strategy—applying grid clustering to cluster core regions while combining grid and Partitioning techniques for edge regions—to enhance accuracy while maintaining efficiency. Experimental results demonstrate that the proposed algorithm outperforms six other benchmark algorithms on datasets with complex shapes and uneven densities, achieving a balance between efficiency and accuracy. Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such as parameter sensitivity, poor adaptability to density variations, and misclassification of edge data. To address these issues, existing research primarily focuses on three directions: (1) optimizing the adaptive selection of grid parameters, which struggles to handle variations in cluster density; (2) improving grid division methods (e.g., multi-granularity or dynamic grids), which have limited effectiveness on complex-shaped data; and (3) integrating other clustering techniques, which enhances accuracy but increases algorithmic complexity. This paper proposes a novel improved grid-based clustering algorithm that determines core grids based on data distribution uniformity rather than absolute density and introduces a clustering strategy for non-core grids based on adjacency relationships. This approach effectively identifies clusters with different densities and reduces dependency on initial parameters (density threshold R and grid partition parameters M). The proposed algorithm integrates grid clustering, partitioning-based clustering, and grid splitting techniques. It employs a regional processing strategy—applying grid clustering to cluster core regions while combining grid and Partitioning techniques for edge regions—to enhance accuracy while maintaining efficiency. Experimental results demonstrate that the proposed algorithm outperforms six other benchmark algorithms on datasets with complex shapes and uneven densities, achieving a balance between efficiency and accuracy. Abstract Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such as parameter sensitivity, poor adaptability to density variations, and misclassification of edge data. To address these issues, existing research primarily focuses on three directions: (1) optimizing the adaptive selection of grid parameters, which struggles to handle variations in cluster density; (2) improving grid division methods (e.g., multi-granularity or dynamic grids), which have limited effectiveness on complex-shaped data; and (3) integrating other clustering techniques, which enhances accuracy but increases algorithmic complexity. This paper proposes a novel improved grid-based clustering algorithm that determines core grids based on data distribution uniformity rather than absolute density and introduces a clustering strategy for non-core grids based on adjacency relationships. This approach effectively identifies clusters with different densities and reduces dependency on initial parameters (density threshold R and grid partition parameters M). The proposed algorithm integrates grid clustering, partitioning-based clustering, and grid splitting techniques. It employs a regional processing strategy—applying grid clustering to cluster core regions while combining grid and Partitioning techniques for edge regions—to enhance accuracy while maintaining efficiency. Experimental results demonstrate that the proposed algorithm outperforms six other benchmark algorithms on datasets with complex shapes and uneven densities, achieving a balance between efficiency and accuracy. |
| ArticleNumber | 18390 |
| Author | He, Honglei |
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| Cites_doi | 10.1145/276304.276314 10.1117/12.2686583 10.1007/s10489-022-03661-7 10.1007/978-3-031-65126-7_16 10.1109/TII.2022.3203721 10.2478/jaiscr-2021-0019 10.1109/ICCUBEA58933.2023.10392256 10.1109/ICAICA58456.2023.10405508 10.1109/ACCESS.2024.3398415 10.1109/EEBDA56825.2023.10090636 10.1109/ICPR.1996.546732 10.3390/ijgi13080286 10.1109/WICT.2012.6409084 10.1007/978-3-031-32910-4_4 10.1007/s12652-022-04428-1 10.1016/j.future.2020.06.053 10.1016/j.ins.2023.120082 10.1016/j.ins.2024.120109 10.3390/electronics13173395 10.11728/cjss2023.05.2022-0054 10.1109/IDAACS-SWS50031.2020.9297060 |
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| Snippet | Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such as... Abstract Grid-based clustering algorithms have become a crucial method in the field of data mining due to their efficiency. However, they face challenges such... |
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| SubjectTerms | 639/705 704/525 Accuracy Adaptability Adjacency relationships Algorithms Clustering Data mining Efficiency Georeferenced clustering Grid clustering Humanities and Social Sciences Hybrid clustering K-means multidisciplinary Science Science (multidisciplinary) |
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| Title | A clustering algorithm based on grids for core data and adjacency relationships for edge data |
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