Affiliations: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, P.R. China
Correspondence:
[*]
Corresponding author. Hong Xue, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, P.R. China. Tel.: +86 010 68985241; Fax: +86 010 68986933; E-mail: [email protected].
Abstract: The early warning classification plays an important role in the emergency management of cluster supply chain. This paper proposed the high-dimensional datastream evolutionary clustering algorithm of early warning classification for cluster supply chain emergency based on cloud model. It solved the bottleneck problem of early warning classification of cluster supply chain emergency with the high-dimensional datastream and composite uncertainty characteristics. The cloud model generation algorithm of early warning summary is used to generate the early warning summary data based on the multiple data fusion method. The evolutionary datastream clustering algorithm of early warning classification is used to dynamically forecast the harming degree of cluster supply chain emergency based on time decaying model and sliding window model. Compared to other similar algorithms, the algorithm proposed in this paper increased the classification accuracy by 92.6% while reduced operation time by 66.7%. The algorithm can provide more accurate decision supports for design and implementation of emergency preplan of cluster supply chain emergency. The feasibility of this algorithm has been demonstrated by multiple experiments conducted on the algorithm.
Keywords: Cloud model generation algorithm of early warning summary, high-dimensional
datastream, composite uncertainty characteristics, evolutionary datastream clustering algorithm of early
warning classification, early warning classification of cluster supply chain emergency
