A biologically-inspired distributed clustering algorithm

Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this paper we propose a distributed clustering algorithm that is inspired by the organization of bee colonies. The performance of our algorithm in terms of the standard F-measure and rand index shows that it is possible to achieve results that are comparable to those from centralized approaches.