Text clustering based on the improved TFIDF by the iterative algorithm
Xingheng Wang, Jun Cao, Yao Liu, Shi Qiao Gao, Xue Feng Deng · 2012
Text clustering, an important part of the machine learning and pattern recognition, has extensive applications in the field of natural language processing. In this paper, a method is given to improve the classic TFIDF algorithm on its shortcomings. This paper classifies the text through Naive Bayesian classifier. And uses the iterative algorithm to optimize the selection of feature words, and then to optimize the classification ceaselessly. Experimental results show that the algorithm has preferable efficiency in feature-select and can increase classification accuracy.