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Parallel gravitational clustering based on grid partitioning for large-scale data

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Abstract

The gravitational clustering algorithm is a dynamic clustering model that achieves outstanding performance in uncovering the hidden clusters of a complex dataset with any shape, density and distribution. This algorithm is very suitable for mining irregular and unbalanced clusters from large-scale datasets with noise. However, the unbearable time overhead makes this algorithm ineffective to apply at large scales. Therefore, a parallel gravitational clustering algorithm based on grid partitioning (PGCGP) is developed in this paper. First, a grid partitioning strategy is designed to divide a large-scale dataset into multiple grids as evenly as possible. Second, a neighbourhood repair strategy is proposed to work with the gravitational clustering algorithm to accurately mine the clusters of a single grid. Finally, a border point alignment strategy is devised to determine whether to merge two small clusters located in different grids to discover the real clusters of the original large dataset by merging multiple grids. Extensive experiments on multiple artificial and real-world datasets verify that our PGCGP approach achieves good performance.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos.62103143 and 61702180); the Hunan Provincial Natural Science Foundation of China (Nos.2020JJ5199 and 2021JJ40214); the National Defense Basic Research Program of China (JCKY2019403D006); the National Key Research and Development Program (No.2019YFE0105300); the Scientific Research Fund of Hunan Provincial Education Department (Nos.20C0786 and 20C0781); and the Hunan Province Science and Technology Project Funds (No.2018TP1036).

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    Authors and Affiliations

    1. School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

      Lei Chen, Fadong Chen, Zhaohua Liu & Mingyang Lv

    2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

      Tingqin He & Shiwen Zhang

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    1. Lei Chen
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    Correspondence to Lei Chen.

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    The MAGIC, SHUTTLE, SKIN, and Poker Hand datasets are the public datasets, they are available in the UCI machine learning repository (http://archive.ics.uci.edu/ml/). The DS1, DS2, DS3, DS4 datasets are available on request from the corresponding author.

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    Fadong Chen, Zhaohua Liu, Mingyang Lv, Tingqin He and Shiwen Zhang contributed equally to this work.

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    Chen, L., Chen, F., Liu, Z. et al. Parallel gravitational clustering based on grid partitioning for large-scale data. Appl Intell 53, 2506–2526 (2023). https://doi.org/10.1007/s10489-022-03661-7

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