This paper provides an implementation of the $C$ -means algorithm in an asynchronous and distributed fashion; specifically, we consider a network of agents, each provided with a piece of information (e.g., data acquired via sensors) and we partition the agents in not mutually exclusive sets such that agents in the same set have similar information; moreover, each set of agents calculates a representative value for the group. Previous distributed algorithms that aimed at accomplishing this task have nontrivial demands, in that they require point-to-point communication capabilities among the agents, which may need to exchange large amounts of data in order to execute their computations. Within the proposed approach, instead, the agents need no prior knowledge about their neighbors and can simply communicate using broadcasts. The proposed solution consists in organizing data transmission following a token-passing approach, thus limiting the communication effort with respect to synchronous distributed implementations; furthermore, the token-passing phase is implemented via the broadcast-only communication, thus avoiding the requirements of the point-to-point communication. As shown via simulations, the latter feature is obtained at the cost of a modest increase in data transmission with respect to a traditional point-to-point token-passing scheme.

中文翻译：

---

通过仅广播令牌传递的分布式C均值聚类

本文提供了 $ C $ -以异步和分布式方式表示算法；具体来说，我们考虑一个代理商网络，每个代理商网络都提供一条信息（例如，通过传感器获取的数据），并且我们将代理商划分为互不排斥的集合，以使同一集合中的代理商具有相似的信息；此外，每组代理都为该组计算一个代表值。旨在完成此任务的先前分布式算法具有非平凡的需求，因为它们需要代理之间的点对点通信功能，这可能需要交换大量数据才能执行其计算。相反，在所提出的方法中，代理不需要任何关于其邻居的先验知识，而可以简单地使用广播进行通信。所提出的解决方案在于按照令牌传递方法来组织数据传输，从而限制了同步分布式实现的通信量。此外，令牌传递阶段是通过仅广播通信实现的，从而避免了点对点通信的需求。如通过仿真所示，相对于传统的点对点令牌传递方案，后一种特征是以数据传输的适度增加为代价而获得的。