Affiliation:
1. Business School, Huanggang Normal University, Huanggang 438000, China
Abstract
In order to effectively ensure the clustering quality of unbalanced big data density, improve the clustering accuracy of unbalanced big data density and shorten the clustering time of unbalanced big data density, an unbalanced big data density clustering method based on dynamic grid is proposed. This paper analyzes the definition and characteristics of dynamic grid, and expounds the clustering method based on density and dynamic grid. Build a dynamic grid of unbalanced big data through unbalanced big data database. Consider a single spatial object, divide the unbalanced big data dynamic grid, and calculate the cell density of the unbalanced big data dynamic grid. Dynamic grid technology is adopted to realize unbalanced large data density clustering. Experimental results show that the proposed algorithm has good clustering effect, which can effectively improve the clustering accuracy of unbalanced big data and shorten the clustering time of unbalanced big data density.
Subject
Artificial Intelligence,Computer Networks and Communications,Software
Reference16 articles.
1. Novel self-adaptive algorithms for non-Lipschitz equilibrium problems with applications;Anh;Journal of Global Optimization,2019
2. A theory of non-equilibrium local search on random satisfaction problems;Aurell;Physical Review Letters,2019
3. Grid-based spatial density visualization and rail transit station prediction;Cai;ISPRS International Journal of Geo-Information,2021
4. A survey on parallel clustering algorithms for big data;Dafir;Artificial Intelligence Review,2020
5. X. Feng, X. Su, X. Lian, M. Xie, P. Liu and F. Jing, Super-resolution reconstruction method for single space object image based on optimized convolution neural network, in: 2019 International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), IEEE, 2019.