Abstract In this paper, we introduce a new evidential clustering algorithm based on finding the belief-peaks and disjoint neighborhoods, called BPDNEC. The basic idea of BPDNEC is that each cluster center has the highest possibility of becoming a cluster center among its neighborhood and neighborhoods of those cluster centers are disjoint in vector space. Such possibility is measured by the belief notion in framework of evidence theory. By solving an equation related to neighborhood size, the size of such disjoint neighborhoods is determined and those objects having highest belief among their neighborhoods are automatically detected as cluster centers. Finally, a credal partition is created by minimizing an objective function concerning dissimilarity matrix of data objects. Experimental results show that BPDNEC can automatically detect cluster centers and derive an appropriate credal partition for both object data and proximity data. Simulations on synthetic and real-world datasets validate the conclusions.

中文翻译：

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通过寻找置信峰值和不相交邻域的证据聚类算法

摘要 在本文中，我们介绍了一种新的基于寻找信念峰值和不相交邻域的证据聚类算法，称为 BPDNEC。BPDNEC 的基本思想是每个聚类中心在其邻域中成为聚类中心的可能性最大，并且这些聚类中心的邻域在向量空间中是不相交的。这种可能性是由证据理论框架中的信念概念来衡量的。通过求解与邻域大小相关的方程，可以确定这种不相交邻域的大小，并且在其邻域中具有最高置信度的对象将被自动检测为聚类中心。最后，通过最小化与数据对象的相异矩阵相关的目标函数来创建凭证分区。实验结果表明，BPDNEC 可以自动检测聚类中心并为对象数据和邻近数据导出合适的凭证分区。对合成数据集和真实数据集的模拟验证了结论。