A Data Mining Method Based on Natural Language Processing and Machine Learning

The data source and data collection method are described, and the results of initial data are presented. And, the method of word segmentation and stop words removal in the preprocessing process is used to clean the data to obtain the data text before clustering. Then, the application scenarios and main characteristics of K-means algorithm are introduced. The algorithm process and calculation methods of the traditional K means algorithm and the improved dichotomous K-means algorithm are introduced in two sections, finally, the experimental text clustering effects of the two algorithms are tested through experiments, and the experimental results are briefly analyzed. It is found that the improved dichotomous K means clustering algorithm has more stable clustering results, and can get more accurate keywords.