[1] |
LU Z. Research on the application of computer data mining technology in the era of big data[J]. Journal of Physics Conference Series, 2021, 1744(4): 042118.
|
[2] |
韩子莹. 大数据技术应用的伦理探究[D]. 北京:北京邮电大学, 2019. HAN Ziying. Ethical exploration of the application of big data technology[D]. Beijing: Beijing University of Posts and Telecommunications, 2019.
|
[3] |
LI Y, WU H. A clustering method based on K-means algorithm[J]. Physics Procedia, 2012, 25: 1104-1109.
|
[4] |
姬强, 孙艳丰, 胡永利, 等. 深度聚类算法研究综述[J]. 北京工业大学学报, 2021, 47(8): 912-924. JI Qiang, SUN Yanfeng, HU Yongli, et al. Review of clustering with deep learning[J]. Journal of Beijing University of Technology, 2021, 47(8): 912-924.
|
[5] |
甘井中, 杨秀兰, 吕洁, 等. 人工智能中无监督学习算法综述[J]. 海峡科技与产业, 2019(1): 134-135. GAN Jingzhong, YANG Xiulan, LÜ Jie, et al. A review of unsupervised learning algorithms in artificial intelligence[J]. Straits Technology and Industry, 2019(1): 134-135.
|
[6] |
任远航. 面向大数据的K-means算法综述[J]. 计算机应用研究, 2020, 37(12): 3528-3533. REN Yuanhang. Survey of K-means algorithm on big data[J]. Application Research of Computers, 2020, 37(12): 3528-3533.
|
[7] |
董文静. K-means算法综述[J]. 信息与电脑, 2021, 33(11): 76-78. DONG Wenjing. Brief survey of K-means clustering algorithms[J]. Information and Computer, 2021, 33(11): 76-78.
|
[8] |
李汉波, 魏福义, 张嘉龙, 等. 基于相异性邻域的改进K-means算法[J]. 现代信息科技, 2021, 5(7): 67-70. LI Hanbo, WEI Fuyi, ZHANG Jialong, et al. Improved K-means algorithm based on dissimilarity neighborhood[J]. Modern Information Technology, 2021, 5(7): 67-70.
|
[9] |
崔丹丹. K-means聚类算法的研究与改进[D]. 合肥:安徽大学, 2012. CUI Dandan. Research and improvement of K-means clustering algorithm[D]. Hefei: Anhui University, 2012.
|
[10] |
董秋仙, 朱赞生. 一种新的选取初始聚类中心的K-means算法[J]. 统计与决策, 2020, 36(16): 32-35. DONG Qiuxian, ZHU Zansheng. A new K-means algorithm for selecting initial clustering center[J]. Statistics & Decision, 2020, 36(16): 32-35.
|
[11] |
郭永坤, 章新友, 刘莉萍, 等. 优化初始聚类中心的K-means聚类算法[J]. 计算机工程与应用, 2020, 56(15): 172-178. GUO Yongkun, ZHANG Xinyou, LIU Liping, et al. K-means clustering algorithm of optimizing initial clustering center[J]. Computer Engineering and Applications, 2020, 56(15): 172-178.
|
[12] |
HEGER J, ABDINE M. Using data mining techniques to investigate the correlation between surface cracks and flange lengths in deep drawn sheet metals[J]. IFAC-PapersOnLine, 2019, 52(13): 851-856.
|
[13] |
FAYYAD U M, REINA C, BRADLEY P S. Initialization of iterative refinement clustering algorithms[J]. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 1998, 8: 194-198.
|
[14] |
DUDA R O, HART P E. Pattern classification and scene analysis[M]. New York: John Wiley & Sons, 1973.
|
[15] |
MALKI N E, RAVAT F. K-means improvement by dynamic pre-aggregates[C] // Proceedings of the 21st International Conference on Enterprise Information Systems. Heraklion, Crete, Greece: ICEIS, 2019: 133-140.
|
[16] |
GENG X, MU Y, MAO S, et al. An improved K-means algorithm based on fuzzy metrics[J]. IEEE Access, 2020, 1(8): 217416-217424.
|
[17] |
ALGULIYEV R M, ALIGULIYEV R M, SUKHOSTAT L V, et al. Parallel batch K-means for big data clustering[J]. Computers & Industrial Engineering, 2021, 152: 107023.
|
[18] |
ANWARY A R, YU H N, VASSALLO M. Gait evaluation using procrustes and euclidean distance matrix analysis[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23: 2021-2029.
|
[19] |
ESTER M. A density-based algorithm for discovering clusters in large spatial databases with noise[J]. AAAI Press, 1996, 96: 226-231.
|
[20] |
JAIN A K, LAW M. Data clustering: a user's dilemma[J]. International Conference on Pattern Recognition & Machine Intelligence, 2005, 3766: 1-10.
|
[21] |
王子龙, 李进, 宋亚飞. 基于距离和权重改进的K-means算法[J]. 计算机工程与应用, 2020, 56(23): 87-94. WANG Zilong, LI Jin, SONG Yafei. Improved K-means algorithm based on distance and weight[J]. Computer Engineering and Applications, 2020,56(23): 87-94.
|