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

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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.

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Notes

  1. https://github.com/alamkashani/KIDS

  2. The probability of data being distributed randomly while we mistakenly consider the data as groups that form clusters.

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Acknowledgements

The authors would like to thank Nasim Bagheri for her generous contribution to English editing and proofreading of the paper. This work was supported by the INFS (Iran National Science Foundation) Grant number 98011279.

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Authors and Affiliations

  1. Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

    Elham S. Kashani & Saeed Bagheri Shouraki

  2. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran

    Yaser Norouzi

  3. KERMIT, Department of Data Analysis and Mathematical Modeling, Ghent University, Ghent, Belgium

    Bernard De Baets

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  1. Elham S. Kashani
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  2. Saeed Bagheri Shouraki
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  3. Yaser Norouzi
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  4. Bernard De Baets
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Correspondence to Elham S. Kashani.

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Kashani, E.S., Bagheri Shouraki, S., Norouzi, Y. et al. A density-grid-based method for clustering k-dimensional data. Appl Intell 53, 10559–10573 (2023). https://doi.org/10.1007/s10489-022-03711-0

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