山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (2): 87-92.doi: 10.6040/j.issn.1672-3961.0.2022.341
侯延琛,赵金东*
HOU Yanchen, ZHAO Jindong*
摘要： 针对K-means聚类算法仅以质心作为聚类依据,在处理非圆球形数据集时效果不理想,数据集的形状特性未得到体现的问题,提出一种基于形状的K-means算法(shape K-means, SPK-means)。将判定点到不同簇中质心以及点到不同簇的最近边缘点的距离作为判定规则,使其具备对任意形状的数据集进行聚类的功能。设置两种不同的数据集进行聚类试验,结果表明,SPK-means聚类算法在处理非圆球形数据集时,其结果遵循原数据集的形状特征。
中图分类号:
| [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. |
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