A statistical signal processing approach to clustering over compressed data
Résumé
In this paper, we consider a network of sensors in which a fusion center applies a clustering method over the sensor measurements. In order to limit their energy consumption, the sensors transmit their measurements in a compressed form. This paper proposes a novel clustering algorithm that applies directly over compressed data, and that does not require the knowledge of the number of clusters. The proposed algorithm is based on a new cost function for centroid estimation, and a theoretical analysis shows that the cluster centroids are the only minimizers of this cost function. The clustering algorithm then estimates the cluster centroids by looking for the minimizers of the cost function, even when their number is unknown. The proposed algorithm shows performance close to that of the K-means algorithm over compressed data, without need to know the number of clusters.