中文摘要：

为了降低数据的存储量和提高系统的处理能力,本文将网络聚类中的相位同步模型和云模型相结合,提出了一种新的数据源的分类方法.首先,将数据源看作一个复杂网络,获得其拓扑结构图,利用模糊层次分析法确定各节点数据源的云模型;然后,计算云模型的期望值、熵和超熵,获得综合耦合强度,将其作为边权;最后,利用相位同步模型对各节点相位进行迭代,获得其分布曲线,进而对各数据源进行分类.本文所提出的方法不仅为数据的存储、清理和压缩提供方便,而且提高了数据的分析效率.

英文摘要：

In order to reduce amount of data storage and improve processing capacity of the system, this paper proposes a new classification method of data source by combining phase synchronization model in network clustering with cloud model. Firstly, taking data source as a complex network, after the topography of network is obtained, the cloud model of each node data is determined by fuzzy analytic hierarchy process （AHP）. Secondly, by calculating expectation, entropy and hyper entropy of the cloud model, comprehensive coupling strength is got and then it is regarded as the edge weight of topography. Finally, distribution curve is obtained by iterating the phase of each node by means of phase synchronization model. Thus classification of data source is completed. This method can not only provide convenience for storage, cleaning and compression of data, but also improve the efficiency of data analysis.