Distributed Clustering and Learning Over Networks

Abstract

Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications, agents may belong to different clusters that pursue different objectives. Then, indiscriminate cooperation will lead to undesired results. In this paper, we propose an adaptive clustering and learning scheme that allows agents to learn which neighbors they should cooperate with and which other neighbors they should ignore. In doing so, the resulting algorithm enables the agents to identify their clusters and to attain improved learning and estimation accuracy over networks. We carry out a detailed mean-square analysis and assess the error probabilities of Types I and II, i.e., false alarm and misdetection, for the clustering mechanism. Among other results, we establish that these probabilities decay exponentially with the step-sizes so that the probability of correct clustering can be made arbitrarily close to one.

Hierarchical Overlapping Clustering of Network Data Using Cut Metrics

“…In this setting, the general objective of clustering is to group those nodes that are more similar to each other than to the rest, according to the relationship established by the dissimilarity function [1], [2]. Clustering and its generalizations are ubiquitous tools since they are used in a wide variety of fields such as psychology [3], social network analysis [4], political science [5], neuroscience [6], among many others [7], [8].…”

Multitask Diffusion Adaptation OverAsynchronous Networks

“…The clustering techniques developed in [30], [31] are based on setting the combination coefficients in an online manner. The technique proposed in [33] is based on solving a hypothesis test problem for setting the neighborhood in an online manner.…”

Adaptive regularized diffusion adaptation over multitask networks

“…We therefore focus on the implementation of diffusion strategies in this article. In particular, we examine networks where different clusters of agents may be interested in different objectives [10][11][12][13][14][15]. In this case, it is important to develop algorithms that enable the agents to continuously learn which of their neighbors belong to the same cluster and which ones are from different clusters.…”

“…References [13,15] study the important case of two objectives where agents receive data from one of two possible models. Reference [16] considers multiple clusters under the assumption that the objectives of adjacent clusters are related to each other and that agents are aware of their clusters.…”