Welcome to the InfoSci Platform
Reference Hub1
An Improved Gravitational Clustering Based on Local Density

An Improved Gravitational Clustering Based on Local Density

Lei Chen, Qinghua Guo, Zhaohua Liu, Long Chen, HuiQin Ning, Youwei Zhang, Yu Jin
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 22
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781799860082|DOI: 10.4018/IJMCMC.2021010101
Cite Article Cite Article

MLA

Chen, Lei, et al. "An Improved Gravitational Clustering Based on Local Density." IJMCMC vol.12, no.1 2021: pp.1-22. http://doi.org/10.4018/IJMCMC.2021010101

APA

Chen, L., Guo, Q., Liu, Z., Chen, L., Ning, H., Zhang, Y., & Jin, Y. (2021). An Improved Gravitational Clustering Based on Local Density. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 12(1), 1-22. http://doi.org/10.4018/IJMCMC.2021010101

Chicago

Chen, Lei, et al. "An Improved Gravitational Clustering Based on Local Density," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 12, no.1: 1-22. http://doi.org/10.4018/IJMCMC.2021010101

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.