Research on Disease and Pest Prediction Model Based on Sparse Clustering Algorithm

Abstract

Clustering is one of the most common technologies in data mining. The size, dimension and sparsity of data are all different aspects that restrict clustering analysis. Clustering machine learning is called unsupervised learning, which is different from classification in that the data objects used in clustering analysis have no class marks, so they need to be calculated by the clustering learning algorithm, while the classified data objects need class marks. At present, most of the sparse data algorithms with high attribute dimensions are oriented to binary data, and there is no evaluation method of clustering results, which brings great limitations to their application. The BP neural network, RBF neural network and Elman neural network model with feedback function, which are mature and widely used in pest prediction models, are studied. The purpose of this paper is to study diseases and pests based on sparse clustering algorithm. In this paper, algorithm formulas, models and data graphs are established to study. From the research, it can be seen that the damage index of pests and diseases is very high, reaching about 50. 54%. Through the research in this paper, we can know that the future will lay a certain foundation for the research of diseases and pests.