Abstract:

Text classification is one of the most important study spots in text mining.In order to overcome the drawback that the classification algorithm based on distance brings high query time cost,a KNearest Neighbors (KNN) algorithm based on the Locality Sensitive Hashing (LSH) of cosine distance under TFIDF is proposed,which can classify Chinese text quickly.Besides,by combing the properties of the text data,experiments with different parameters are carried out.In the experiments,boolean vectors are used to avoid the duplication calculation.Compared with the original KNN,our algorithm can increase the speed of classification in ensuring the accuracy.

Key words: text classification;Locality Sensitive Hashing (LSH);TF-IDF;KNN;boolean vector