FASTBEE: A Fast and Self-Adaptive Clustering Algorithm Towards to Edge Computing

摘要

With the rapid development of data explosion era, the cloud computing has been unable to process the massive data efficiently which forced on a urgent need for edge computing. Edge computing refers to a new type of computing model that performs computations on a large amount of data at the edge of a network. In order to improve the operating efficiency in the network, we put forward that applying a fast and self-adaptive clustering algorithm to the edge computing which helps the edge devices to distribute different types of data clustered to the cloud computing center. In our paper, we proposed the FASTBEE algorithm which is suitable for edge computing. The FASTBEE algorithm makes improvements on density collision and dynamic determination of density threshold by using gradient descent method to update the sum of squared errors formula. The proposed algorithm is extensively tested on several well-known datasets. The results proved the performance of our approach that its accuracy is 36 percent higher and it runs much faster compared with the CFSFDP algorithm and the DBSCAN algorithm.