An Edge-Cloud-Aided High-Order Possibilistic c-Means Algorithm for Big Data Clustering

In this article, a high-order possibilistic c-means algorithm (HOPCM) based on the double-layer deep computation model (DCM) is proposed for big data clustering. Specifically, an asymmetric tensor autoencoder is presented to efficiently train the double-layer DCM for big data feature learning. Furthermore, an edge-cloud computing system is developed to improve the clustering efficiency. In the edge-cloud system, the computation-intensive tasks including the parameters' training and clustering are offloaded to the cloud while the task of feature learning is performed at the edge of network. Finally, we conduct extensive experiments to evaluate the performance of the presented algorithm by comparing it with other two representative big data clustering algorithms, i.e., the standard HOPCM and the HOPCM based on deep learning. Results demonstrate that the presented algorithm achieves higher accuracy than the two compared algorithms and furthermore the clustering efficiency are significantly improved by the developed edge-cloud computing system.