A MULTIAGENT COORDINATION APPROACH TO ROBUST CONSENSUS CLUSTERING

In many distributed modeling, control or information processing applications, clustering patterns that share certain similarities is the critical first step. However, many traditional clustering algorithms require centralized processing, reliable data collection and the availability of all the raw data in one place at one time. None of these requirement can be met in many complex real world problems. In this paper, we present an agent-based method for combining multiple base clusterings into a single unified "consensus" clustering that is robust against many types of failures and does not require spatial/temporal synchronization. In this approach, agents process clusterings coming from separate sources and pool them to produce a unified consensus. The first contribution of this work is to provide an adaptive method by which the agents update their selections to maximize an objective function based on the quality of the consensus clustering. The second contribution of this work is in providing intermediate agent-specific objective functions that significantly improve the quality of the consensus clustering process. Our results show that this agent-based method achieves comparable or better performance than traditional non-agent consensus clustering methods in fault-free conditions, and remains effective under a wide range of failure scenarios that paralyze the traditional methods.