Ensemble Learning for Cluster Number Detection Based on Shared Nearest Neighbor Graph and Spectral Clustering
Ensemble Learning for Cluster Number Detection Based on Shared Nearest Neighbor Graph and Spectral Clustering
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DOI：
10.1109/ijcnn55064.2022.9892958
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Weihang Zhang;Xiucai Ye;Testuya Sakurai
中科院分区：
文献类型：
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作者：
Weihang Zhang;Xiucai Ye;Testuya Sakurai
检测聚类的数量对于聚类分析是很重要的。许多现有方法通过预定义候选簇号列表来检测簇号。然而，如果候选簇号没有很好地预定义，则不能正确地检测簇号。本文提出了一种新的基于多个共享最近邻图的聚类方法，该方法自动生成候选聚类个数和相应的簇划分。然后通过集成学习从聚类分区中恢复共享的低阶相似度矩阵。最后，利用候选聚类数对共享的低阶相似度矩阵进行谱聚类，从而检测出聚类数。在人工数据集和真实数据集上的实验结果表明，该方法不仅能够正确地检测聚类个数，而且与现有方法相比，获得了更好的聚类结果。
Detecting the number of clusters is important for cluster analysis. Many existing methods detect the cluster number by predefining a list of candidate cluster numbers. However, if the candidate cluster numbers are not well predefined, the cluster number cannot be correctly detected. In this paper, we propose a novel clustering method which automatically generates the candidate cluster numbers and the corresponding cluster partitions based on multiple shared nearest neighbor graphs. A shared low-rank similarity matrix is then recovered from the cluster partitions by ensemble learning. Finally, spectral clustering is applied on the shared low-rank similarity matrix with the candidate cluster numbers to detect the cluster number. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method not only correctly detects the cluster numbers, but also obtains better clustering results in comparison to the existing methods.