Information Entropy-based Density Clustering Algorithm of Database Log

The rapid development of the Internet has brought new directions for improvement in e-commerce and promoted the pace of e-commerce to smart business. There are a lot of articles focusing on improving business processes efficiency, especially on business process nodes' optimization, using machine learning and even deep learning methods. Aiming at the problem of chaotic nodes' arrangement in traditional business process, this paper proposes a node optimization method, which can reconfigure business process nodes-integrating similar business nodes and decomposing complex business nodes. The algorithm proposed in this paper firstly vectorizes the process nodes to n-dimensional vectors, then uses the improved density clustering mining algorithm which is based on information entropy of clustering results to reassign the nodes. The information entropy can fairly evaluate the degree of system's confusion, so we can introduce information entropy to density clustering, then the clustering result will be evaluated and the algorithm will iteratively cluster until an optimal result is achieved. Finally, this paper conducted two experimental analysis on four business processes, successfully completed the optimal configuration of the nodes.