Incremental Subspace Data-mining Algorithm Based on Data-flow Density of Complex Network

In order to improve the accuracy of data-mining in large-scale complex networks, an incremental subspace data-mining algorithm based on data-flow density of complex network is proposed in this paper. In order to accommodate this goal, a latent variable model is first introduced and incorporated into Data-flow density model so that the network is divided into different communities and the defect of traditional data-mining can be explained. The undirected traversing loop is used to determine their corresponding communities for data-flow. The incremental subspace data-mining algorithm is also used to calculate the correlation between community network and data-flow and the correlation coefficient between data-flow node and time so as to accurately determine the destination of the object data-flow. In the case of a larger number of nodes and communities, data analysis and simulation experiment results demonstrate that the incremental subspace data-mining algorithm still has a higher mining accuracy.