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In sensor networks, it is not always practical to set up a fusion center.Therefore, there is need for fully decentralized clustering algorithms.Decentralized clustering algorithms should minimize the amount of dataexchanged between sensors in order to reduce sensor energy consumption. In thisrespect, we propose one centralized and one decentralized clustering algorithmthat work on compressed data without prior knowledge of the number of clusters.In the standard K-means clustering algorithm, the number of clusters isestimated by repeating the algorithm several times, which dramaticallyincreases the amount of exchanged data, while our algorithm can estimate thisnumber in one run.  The proposed clustering algorithms derive from a theoretical frameworkestablishing that, under asymptotic conditions, the cluster centroids are theonly fixed-point of a cost function we introduce. This cost function depends ona weight function which we choose as the p-value of a Wald hypothesis test.This p-value measures the plausibility that a given measurement vector belongsto a given cluster. Experimental results show that our two algorithms arecompetitive in terms of clustering performance with respect to K-means andDB-Scan, while lowering by a factor at least $2$ the amount of data exchangedbetween sensors.

Decentralized Clustering on Compressed Data without Prior Knowledge of the Number of Clusters