Distributed Robust Bayesian Cluster Enumeration Criterion for Unsupervised Learning

Abstract

Wireless sensor networks have been widely deployed for industrial and consumer applications. The amount of data in such applications is large, and as a results a result, the automatic discovery of the underlying structure in the data (cluster analysis) becomes of prominent interest. A challenging task in cluster analysis is the estimation of the number of clusters. To this end, we propose a robust decentralized diffusion-based cluster enumeration method that enables distributed sensor nodes to estimate the number of clusters in their respective data sets through cooperation with their immediate neighbors. The proposed method is robust to the presence of heavy-tailed noise and outliers, which is useful for sensor networks as outliers can occur due to measurement errors or sensor failure. Through experiments, we show that the proposed method is promising, and achieves the performance of a centralized network using a fusion center.