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A density-grid-based method for clustering k-dimensional data

Elham S. Kashani, Saeed Bagheri Shouraki, Yaser Norouzi and Bernard De Baets (UGent)
(2023) APPLIED INTELLIGENCE. 53(9). p.10559-10573
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Abstract
In this paper, we propose a novel density-grid-based method for clustering k-dimensional data. KIDS, an acronym for K-dimensional Ink Drop Spread, detects densely-connected pieces of data in k-dimensional grids. It enables one to simultaneously exploit the advantages of fuzzy logic, as well as both density-based and grid-based clustering. In the proposed method, the k-dimensional data space is divided into different cells. Input data records are mapped to the cells. The data points are then spread in the k-dimensional cells, just like what happens to ink drops in water. So the cells adjacent to the data cells also represent the data. Eventually, the impacts of all data grid cells are condensed and compared with the threshold to compute the final clusters. The experimental results show that the method has superior quality and efficiency in both low and high dimensions. In addition, the method is not only robust to noise but it is also capable of finding clusters of arbitrary shapes.
Keywords
Artificial Intelligence, Clustering, Density-grid-based method, k-dimensional data, Diffusion, Aggregation, ALGORITHM

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Citation

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MLA
Kashani, Elham S., et al. “A Density-Grid-Based Method for Clustering k-Dimensional Data.” APPLIED INTELLIGENCE, vol. 53, no. 9, 2023, pp. 10559–73, doi:10.1007/s10489-022-03711-0.
APA
Kashani, E. S., Bagheri Shouraki, S., Norouzi, Y., & De Baets, B. (2023). A density-grid-based method for clustering k-dimensional data. APPLIED INTELLIGENCE, 53(9), 10559–10573. https://doi.org/10.1007/s10489-022-03711-0
Chicago author-date
Kashani, Elham S., Saeed Bagheri Shouraki, Yaser Norouzi, and Bernard De Baets. 2023. “A Density-Grid-Based Method for Clustering k-Dimensional Data.” APPLIED INTELLIGENCE 53 (9): 10559–73. https://doi.org/10.1007/s10489-022-03711-0.
Chicago author-date (all authors)
Kashani, Elham S., Saeed Bagheri Shouraki, Yaser Norouzi, and Bernard De Baets. 2023. “A Density-Grid-Based Method for Clustering k-Dimensional Data.” APPLIED INTELLIGENCE 53 (9): 10559–10573. doi:10.1007/s10489-022-03711-0.
Vancouver
1.
Kashani ES, Bagheri Shouraki S, Norouzi Y, De Baets B. A density-grid-based method for clustering k-dimensional data. APPLIED INTELLIGENCE. 2023;53(9):10559–73.
IEEE
[1]
E. S. Kashani, S. Bagheri Shouraki, Y. Norouzi, and B. De Baets, “A density-grid-based method for clustering k-dimensional data,” APPLIED INTELLIGENCE, vol. 53, no. 9, pp. 10559–10573, 2023.
@article{01H39C4DNKZH861K163HCNGD9K,
  abstract     = {{In this paper, we propose a novel density-grid-based method for clustering k-dimensional data. KIDS, an acronym for K-dimensional Ink Drop Spread, detects densely-connected pieces of data in k-dimensional grids. It enables one to simultaneously exploit the advantages of fuzzy logic, as well as both density-based and grid-based clustering. In the proposed method, the k-dimensional data space is divided into different cells. Input data records are mapped to the cells. The data points are then spread in the k-dimensional cells, just like what happens to ink drops in water. So the cells adjacent to the data cells also represent the data. Eventually, the impacts of all data grid cells are condensed and compared with the threshold to compute the final clusters. The experimental results show that the method has superior quality and efficiency in both low and high dimensions. In addition, the method is not only robust to noise but it is also capable of finding clusters of arbitrary shapes.}},
  author       = {{Kashani, Elham S. and Bagheri Shouraki, Saeed and Norouzi, Yaser and De Baets, Bernard}},
  issn         = {{0924-669X}},
  journal      = {{APPLIED INTELLIGENCE}},
  keywords     = {{Artificial Intelligence,Clustering,Density-grid-based method,k-dimensional data,Diffusion,Aggregation,ALGORITHM}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{10559--10573}},
  title        = {{A density-grid-based method for clustering k-dimensional data}},
  url          = {{http://doi.org/10.1007/s10489-022-03711-0}},
  volume       = {{53}},
  year         = {{2023}},
}
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