Data Clustering as an Emergent Consensus of Autonomous Agents
This work addresses data clustering problems for researchers in machine learning and computer vision, but it appears incremental as it builds on existing methods like DBSCAN.
The paper tackles data segmentation by proposing a first-order density-induced consensus protocol, providing a rigorous mathematical analysis and applying it to shape datasets and images from the Berkeley Segmentation Dataset, showing it as an augmentation of classical clustering techniques like DBSCAN.
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.