计算机科学
聚类分析
光谱聚类
数据挖掘
人工智能
模式识别(心理学)
作者
Runxin Zhang,S Hang,Zhensheng Sun,Feiping Nie,Rong Wang,Xuelong Li
标识
DOI:10.1016/j.inffus.2024.102587
摘要
Ensemble clustering can obtain better and more robust results by fusing multiple base clusterings, which has received extensive attention. Although many representative algorithms have emerged in recent years, this field still has two tricky problems. First, spectral clustering can identify clusters of arbitrary shapes, but the high time and space complexity limit its application in generating base clusterings. Most existing algorithms utilize k-means to generate base clusterings, and the clustering effect on nonlinearly separable datasets needs further improvement. Second, ensemble clustering algorithms should generate multiple base clusterings. Even if low-complexity algorithms are applied, the running time is also long, which seriously affects the application of ensemble clustering algorithms on large-scale datasets. To tackle these problems, we propose a fast K-nearest neighbors approximation method, construct an anchor graph to approximate the similarity matrix, and use singular value decomposition (SVD) instead of eigenvalue decomposition (EVD) to reduce the time and space complexity of conventional spectral clustering. At the same time, we obtain multiple base clusterings by running spectral embedding once. Finally, we convert these base clusterings into a bipartite graph and use transfer cut to get the final clustering results. The proposed algorithms significantly reduce the running time of ensemble clustering. Experimental results on large-scale datasets fully prove the efficiency and superiority of our proposed algorithm.
科研通智能强力驱动
Strongly Powered by AbleSci AI