For unsupervised learning, we propose a new clustering method which incorporates belief peaks into a linear label propagation strategy. The proposed method aims to reveal the data structure by finding out the exact number of clusters and deriving a fuzzy partition. Firstly, the cluster centers and outliers can be identified by the improved belief metric, which makes use of the whole data distribution information so as to correctly highlight the cluster centers without the limitation of massive neighbor points. Secondly, an informative initial fuzzy cluster assignment for each remaining point is created by considering the distances between its neighbors and each cluster center, then the fuzzy label of each point will be iteratively updated by absorbing its neighbors’ label information until the fuzzy partition is stable. The label propagation assignment strategy provides a valuable alternative technique with explicit convergence and linear complexity in the field of belief-peaks clustering. The effectiveness of the proposed method is tested on seven commonly used real-world datasets from the UCI Machine Learning Repository, and seven synthetic datasets in the domain of data clustering. Comparing with several state-of-the-art clustering methods, the experiments reveal that the proposed method enhanced the clustering results in terms of the exact numbers of clusters and the Adjusted Rand Index. Further, the parameter analysis experiments validate the robustness to the two tunable parameters in the proposed method.

中文翻译：

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基于模糊标签传播的信念峰聚类

对于无监督学习，我们提出了一种新的聚类方法，该方法将置信度峰值合并到线性标签传播策略中。所提出的方法旨在通过找出簇的确切数目并得出模糊分区来揭示数据结构。首先，可以通过改进的置信度来识别聚类中心和离群值，该置信度度量利用了整个数据分布信息，从而可以正确地突出显示聚类中心，而不受大量相邻点的限制。其次，通过考虑相邻点与每个聚类中心之间的距离，为每个剩余点创建一个信息量大的初始模糊聚类分配，然后通过吸收相邻点的标记信息来迭代更新每个点的模糊标记，直到模糊分区稳定为止。标签传播分配策略在信念峰聚类领域中提供了一种具有显着收敛性和线性复杂度的有价值的替代技术。在UCI机器学习存储库中的七个常用的现实世界数据集以及数据聚类领域中的七个综合数据集上，测试了该方法的有效性。与几种最新的聚类方法相比，实验表明，该方法在聚类的确切数目和兰特调整指数方面增强了聚类结果。此外，参数分析实验验证了所提出方法对两个可调参数的鲁棒性。在UCI机器学习存储库中的七个常用的现实世界数据集以及数据聚类领域中的七个综合数据集上，测试了该方法的有效性。与几种最新的聚类方法相比，实验表明，该方法在聚类的确切数目和兰特调整指数方面提高了聚类结果。此外，参数分析实验验证了所提出方法对两个可调参数的鲁棒性。在UCI机器学习存储库中的七个常用的现实世界数据集以及数据聚类领域中的七个综合数据集上，测试了该方法的有效性。与几种最新的聚类方法相比，实验表明，该方法在聚类的确切数目和兰特调整指数方面增强了聚类结果。此外，参数分析实验验证了所提出方法对两个可调参数的鲁棒性。实验表明，该方法在聚类的准确数目和兰德调整指数方面提高了聚类结果。此外，参数分析实验验证了所提出方法对两个可调参数的鲁棒性。实验表明，该方法在聚类的准确数目和兰德调整指数方面提高了聚类结果。此外，参数分析实验验证了所提出方法对两个可调参数的鲁棒性。