Gaussian Naive Bayesian Data Classification Model Based on Clustering Algorithm

摘要

A gaussian naive bayesian data classification model based on clustering algorithm was proposed for fast recognition and classification of unknown continuous data containing a large number of non-priori knowledge. Firstly, the unknown data were extracted from the representative samples according to the information entropy measure for clustering to generate class labels. Then, the mapping relationship between data and class labels was established by using the gaussian naive bayes algorithm, and the classification model was obtained through training. Simulation results show that this unsupervised analysis process has a good classification effect on new data.