A text clustering algorithm based on simplified cluster hypothesis

How to quickly and efficiently determine the subject category from a large amount of text is becoming an important challenge in text clustering. In this paper, One-Next text clustering algorithm based on the simplified cluster hypothesis is proposed. Meanwhile, a feature vector optimization method using grading feature vector extraction method is designed. Finally, the experimental results show that this method can get a high precession and F value, and the algorithm complexity is lower than other text clustering methods.