Research on self-adaptive clustering algorithms for large data sparse networks based on information entropy

Abstract

With the advent of the era of artificial intelligence and information technology, a large number of data and information pour into all walks of life. These data packages include many online and offline data such as text files, audio and video. However, so many data are unnecessary in real life. The application of data clustering algorithm based on artificial intelligence technology can solve such problems very well. However, the traditional clustering algorithm relies too much on manual operation when choosing clustering centers, which greatly reduces the efficiency of the whole algorithm. At the same time, the traditional clustering algorithm based on sparse network has too many coefficients in its coefficient matrix design, so it can not aggregate the relevant data well. This paper will measure the correlation of related data based on information entropy, and innovatively improve the existing sparse data network model. A model training algorithm based on multi-strategy pattern optimization is proposed to realize the automatic selection of clustering centers and reduce the training time of the algorithm. In terms of data clustering correlation, this paper proposes an optimized adaptive clustering algorithm based on the joint model of sparse subspace clustering algorithm model and the norm of adaptive subspace segmentation. In the experimental part, this paper compares the proposed algorithm with the traditional density peak clustering algorithm. The experimental results show that the proposed algorithm has obvious advantages in text data collection and classification, image data collection and filtering.