Study on Text Clustering Based on Semantic Density

Combined with semantic similarity of text data,this paper gives a method of text data clustering based on semantic density.According to the characteristics of text data,from a randomly selected text object,it expands towards the most intensive area of the text data,organizes into a structure to reflect the corpus in an orderly sequence,and then clusters.In dealing with noise text data,it uses the results of the reorganization of an effective strategy to support the re-positioning noise text data.Experimental results show that the method has good clustering performance.