论文标题
数据聚类作为自主代理的新兴共识
Data Clustering as an Emergent Consensus of Autonomous Agents
论文作者
论文摘要
我们提出了基于一阶密度诱导共识方案的数据分割方法。我们对共识模型进行了数学上的严格分析，从而导致数据分割算法的停止标准。为了说明我们的方法，将算法应用于二维形状数据集和伯克利分割数据集中选定的图像。该方法可以看作是用于多模式特征空间（例如DBSCAN）的经典聚类技术的增强。它展示了数据聚类和集体行为之间的奇怪联系。
We present a data segmentation method based on a first-order density-induced consensus protocol. We provide a mathematically rigorous analysis of the consensus model leading to the stopping criteria of the data segmentation algorithm. To illustrate our method, the algorithm is applied to two-dimensional shape datasets and selected images from Berkeley Segmentation Dataset. The method can be seen as an augmentation of classical clustering techniques for multimodal feature space, such as DBSCAN. It showcases a curious connection between data clustering and collective behavior.
