
研究方向:机器学习、数据挖掘、多粒度认知计算、粒球计算、聚类分析、异常检测
主要科研成果:
代表性论著
[1] Cheng D, Jiang X, Xia S, Wang G, Huang J, Zhang S, Wang Y. Fast Spectral Clustering via Pseudo-label-Based Granular-ball Division for Large-scale Data[J]. IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2026.3672824. (CCF A)
[2] Cheng D, Jiang X, Xia S, Wang G. Pseudo-label-Based Unsupervised Granular-ball Division and Fast Spectral Clustering for High-dimensional Data[C]. 2025 IEEE 41st International Conference on Data Engineering (ICDE), 4106-4119. (CCF A会)
[3] Cheng D, Liu S, Xia S, Wang G. Granular-ball computing-based manifold clustering algorithms for ultra-scalable data[J]. Expert Systems With Applications, 2024, 247, 123313. 17 doi: 10.1016/j.eswa.2024.123313
[4] Cheng D, Zhang C, Li Y, Xia S, Wang G, Huang J, Zhang S, Xie J. GB-DBSCAN: A fast granular-ball based DBSCAN clustering algorithm[J]. Information Sciences, 2024, 674, 120731. 18 doi: 10.1016/j.ins.2024.120731
[5] Cheng D, Huang J, Zhang S, Xia S, Wang G, Xie J. K-means clustering with natural density peaks for discovering arbitrary-shaped clusters[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(8): 11077-11090. DOI: 10.1109/TNNLS.2023.3248064. 51
[6] Cheng D, Li Y, Xia S, Wang G, Huang J, Zhang S. A fast granular-ball-based density peaks clustering algorithm for large scale data[J], IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(12): 17202-17215. doi: 10.1109/TNNLS.2023.3300916. 68
[7] Cheng D, Luo J, Huang J, Zhang S. Searching natural neighbors in an accelerated way[J]. Engineering Applications of Artificial Intelligence, 2023, 126, 107172.
[8] Cheng D, Huang J, Zhang S, Zhang X, Luo X. A novel approximate spectral clustering algorithm with dense cores and density peaks [J], IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(4):2348-2360. DOI: 10.1109/TSMC.2021.3049490.
[9] Cheng D, Zhu Q, Huang J, Wu Q, Yang L, Clustering with local density peaks-based minimum spanning tree[J], IEEE Transactions on Knowledge and Data Engineering, 2021, 33(2), 374-387. DOI: 10.1109/TKDE.2019.2930056. (CCF-A)
[10] Cheng D, Zhu Q, Huang J, Wu Q, Yang L, A novel cluster validity index based on local cores [J], IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(4): 985-999. DOI: 10.1109/TNNLS.2018.2853710.
[11] Cheng D, Zhang S, Huang J. Dense members of local cores-based density peaks clustering algorithm [J], Knowledge-Based Systems, 2020, 193: 105454.
[12] Huang J, Cheng D*, Zhang S. A novel outlier detecting algorithm based on the outlier turning points[J]. Expert Systems with Applications, 2023, 231: 120799. doi: 10.1016/j.eswa.2023.120799
[13] 程东东,黄金龙,朱庆生. 基于自然邻居的聚类分析和离群检测算法研究[M]. 上海交通大学出版社,2019.11.
项目
1. 国家自然科学基金青年科学基金项目:62006029,基于局部核心点的高光谱图像聚类研究,2021. 01-2023. 12, 24万元,结题,主持
2. 重庆市博士后创新人才支持计划:CQBX2021024,基于局部核心点的多粒度模型构建研究,2022.1-2024.12,60万元,结题,主持
3. 重庆市自然科学基金面上项目:cstc2019jcyj-msxmX0683, 基于局部核心点聚类分析的3D点云骨架提取研究,2019.07-2022.06, 10万元,结题,主持
4. 重庆市教委科技项目重点项目:KJZD-K202301402,基于多粒度表示的高光谱聚类研究,2023.10-2026.10,在研,主持
5. 重庆市自然科学基金面上项目:CSTB2022NSCQ-MSX0258,基于复杂流形聚类的高光谱图像波段选择方法研究,2022.8-2025.3, 10万元,结题,主持
6. 重庆市自然科学基金面上项目:CSTB2025NSCQ-GPX0420,面向大规模多视图数据的粒球聚类研究,2025.7-2028.6, 10万元,在研,主持
7. 重庆市教委科技项目青年项目:KJQN202001434,基于数据挖掘的树木骨架提取研究,2020.10-2023.10,结题,主持
8. 国家自然科学基金委员会,面上项目,62376045,面向复杂任务场景的多粒度群体智能模型与算法研究,2024-01至2027-12,50万元,负责人:王国胤,参与人:程东东。
专利
1. 程东东,黄驰原,黄金龙,张素兰,胡新,桂俊,国家发明专利,一种基于3D点云的树木骨架提取方法,ZL 2021 1 1231714.X,2024年7月2日.
2. 程东东,一种基于并行自然邻居的局部核心点聚类算法,国外授权发明专利,LU506079,2024年7月10日
获奖
1. 程东东、朱庆生、陈叶旺、黄金龙、冯骥,基于自然邻居的大数据快速聚类与异常检测研究,2022年度重庆市自然科学奖三等奖
2. 程东东,朱庆生,黄金龙,吴全旺,杨利军,A Novel Cluster Validity Index Based on Local Cores,四川省科学技术协会,重庆市科学技术协会,首届川渝科技学术大会优秀论文三等奖,2020
3. 程东东,黄金龙,张素兰,A Novel Approximate Spectral Clustering Algorithm With Dense Cores and Density Peaks, 涪陵区第十三届自然科学优秀学术论文二等奖。2023. 12.
4. 程东东,Clustering with Local Density Peaks-Based Minimum Spanning Tree,涪陵区第十二届自然科学优秀学术论文三等奖,2021.12。
5. 刘舒姝, 程东东, 谢江,Granular-ball-based Fast Spectral Embedding Clustering Algorithm for Large-Scale Data, 获得16th International Conference on Machine Learning and Computing 最佳学生论文奖,2024.12。(指导的硕士研究生获奖)
联系方式:cdd@yznu.edu.cn