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An Improved Gravitational Clustering Based on Local Density
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Author(s): Lei Chen (School of Information and Electrical Engineering, Hunan University of Science and Technology, China), Qinghua Guo (School of Information and Electrical Engineering, Hunan University of Science and Technology, China), Zhaohua Liu (State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, China), Long Chen (School of Information and Electrical Engineering, Hunan University of Science and Technology, China), HuiQin Ning (School of Information and Electrical Engineering, Hunan University of Science and Technology, China), Youwei Zhang (School of Information and Electrical Engineering, Hunan University of Science and Technology, China)and Yu Jin (School of Information and Electrical Engineering, Hunan University of Science and Technology, China)
Copyright: 2021
Volume: 12
Issue: 1
Pages: 22
Source title:
International Journal of Mobile Computing and Multimedia Communications (IJMCMC)
Editor(s)-in-Chief: Agustinus Waluyo (Monash University, Australia)
DOI: 10.4018/IJMCMC.2021010101
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Abstract
Gravitational clustering algorithm (Gravc) is a novel and excellent dynamic clustering algorithm that can accurately cluster complex dataset with arbitrary shape and distribution. However, high time complexity is a key challenge to the gravitational clustering algorithm. To solve this problem, an improved gravitational clustering algorithm based on the local density is proposed in this paper, called FastGravc. The main contributions of this paper are as follows. First of all, a local density-based data compression strategy is designed to reduce the number of data objects and the number of neighbors of each object participating in the gravitational clustering algorithm. Secondly, the traditional gravity model is optimized to adapt to the quality differences of different objects caused by data compression strategy. And then, the improved gravitational clustering algorithm FastGravc is proposed by integrating the above optimization strategies. Finally, extensive experimental results on synthetic and real-world datasets verify the effectiveness and efficiency of FastGravc algorithm.
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