基于K-means和TF-IDF的中文药名聚类分析

Chinese drug name cluster analysis based on K-means and TF-IDF

针对药名聚类中药物命名特殊性导致的命名准确率低的问题,提出了基于TF-IDF和K-means的药名聚类方法。药物命名具有一定的规律性且中西药名命名形式不同等特点,基于字词共现频率的方法难以取得较好的聚类效果,因此,使用TF-IDF方法计算药名相似的方法并采用K-means聚类算法进行药名的聚类。实验结果表明,TFIDF的聚类准确率高于TF的聚类方法,按字切分的聚类准确率高于分词后的聚类准确率,基于字和TF-IDF的聚类准确率最高且稳定,准确率达到96.77%。

Because of the problem of low accuracy of Chinese name clustering, the method of durg name clustering based on TF-IDF （Term Frequency-Inverse Document Frequency） and K- means was proposed. As the durg name is with a certain regularity and western medicine is named in different forms, it＇s difficult to obtain better clustering results based on word co- occurrence frequency, so, TF-IDF method was used to identify similar drug names and K- means clustering algorithm was used for clustering drug names. Experimental results show that TF-IDF clusters drug names with high accuracy, the clustering of word-segmentation has higher accuracy than the clustering of participle. The clustering of words and TF-IDF has the higher accuracy and stablility, and its aeeuraey rate reaches 96.77%.