DSCPL: A Deep Cloud Manufacturing Service Clustering Method Using Pseudo-Labels

Abstract

Research on service clustering for manufacturing network by deep learning is an effective method for service discovery and service management in manufacturing industries. Manufacturing service clustering can be used to solve the problem of service selection and composition in cloud manufacturing. After clustering characteristics of graph data, this paper analyzes and studies the manufacturing network structure, and finds the network characteristics formed by clustering. A deep manufacturing cloud service clustering model using pseudo-labels (DSCPL) is proposed, which combines graph topology and node features to cluster nodes with similar attributes. The specific contributions of the paper are as follows: 1) Defining a parametric nonlinear map embedded the graph space containing graph structure (adjacency information) and node information (features) into a low-dimensional feature space. 2)Building an auxiliary target distribution to realize the self-learning mechanism in order to adapt to the clustering task, and 3) Realizing the unsupervised clustering method. Comprehensive experiments show that the clustering effect of this method is better than the current advanced deep clustering methods on public datasets and simulated datasets.