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An Edge-Cloud-aided High-order Possibilistic c-Means Algorithm for Big Data Clustering
IEEE Transactions on Fuzzy Systems ( IF 11.9 ) Pub Date : 2020-12-01 , DOI: 10.1109/tfuzz.2020.2992634
Fanyu Bu , Qingchen Zhang , Laurence T. Yang , Hang Yu

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.

中文翻译:

一种用于大数据聚类的边缘云辅助高阶可能性 c 均值算法

在本文中,提出了一种基于双层深度计算模型(DCM)的高阶可能性c-means算法(HOPCM)用于大数据聚类。具体来说,提出了一种非对称张量自动编码器,以有效地训练双层 DCM 进行大数据特征学习。此外,还开发了边缘云计算系统以提高聚类效率。在边缘云系统中,包括参数训练和聚类在内的计算密集型任务被卸载到云端,而特征学习的任务在网络边缘执行。最后,我们进行了广泛的实验,通过将其与其他两种具有代表性的大数据聚类算法,即标准 HOPCM 和基于深度学习的 HOPCM 进行比较,来评估所提出算法的性能。
更新日期:2020-12-01
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