工业物联网数据流自适应聚类方法

5G通讯技术的迅猛发展使工业物联网得到了全面提升, 工业物联网数据规模将越来越大、数据维度也越来越高, 如何高效利用流聚类进行工业物联网数据挖掘工作是一个亟需解决的问题. 提出了一种基于工业物联网数据流自适应聚类方法. 该算法利用微簇之间的高密性, 计算各微簇节点的局部密度峰值以自适应产生宏簇数; 采用引力能量函数对微集群进行递归在线更新; 并且去除边缘相交微簇之间的计算以达到降低维护宏簇所需的计算量. 理论分析和实验对比表明所提出的方法跟当前主流的流聚类算法相比有着更高质量的聚类效果.

Adaptive Clustering Method of Industrial Internet of Things Data Stream

The rapid development of 5G communication technology has led to a comprehensive enhancement of the industrial Internet of Things (IIoT). The scale of IIoT data will become larger, and the dimensionality of data will become higher. As a result, how to efficiently use stream clustering for IIoT data mining is an urgent problem. In this regard, this paper proposes an adaptive clustering method of an IIoT data stream. The algorithm exploits the high density between micro-clusters and calculates the local density peaks of each micro-cluster node to adaptively generate the number of macro-clusters. It employs a gravitational energy function to recursively update the micro-clusters on line and removes the computation between edge-intersecting micro-clusters to achieve a reduction in the computational effort required to maintain the macro-clusters. The theoretical analysis and experimental comparison show that the proposed method has higher-quality clustering results than the current mainstream stream clustering algorithms.