摘要
针对传统词频-逆文档频率(TF-IDF)算法对具有特定属性的文本分类存在的不足,尤其是词汇在特定分类中具有特殊意义情形下准确率较低的问题,提出一种改进的TF-IDF文本聚类算法.采用2015—2019年吉林省科研机构发表论文数据进行对比实验,分别用改进TF-IDF算法和传统TF-IDF算法先统计论文中的关键词词频,再通过K-means++算法进行聚类,最后使用随机森林算法分别评估聚类的准确性.实验结果表明,改进TF-IDF算法提高了分类的准确率.
Aiming at the shortcomings of traditional term frequency-inverse document frequency(TF-IDF)algorithm for text classification with specific attributes,especially the low accuracy of words with specific meaning under specific classification,we proposed an improved TF-IDF text clustering algorithm.Comparative experiments were carried out through the papers published by scientific research institutions in Jilin Province from 2015 to 2019.The improved TF-IDF algorithm and the traditional TF-IDF algorithm were used to calculate the frequency of keywords in the papers,then K-means++method was used to cluster.Finally,random forest algorithm was used to evaluate the accuracy of clustering.The experimental results show that the improved TF-IDF algorithm improves the accuracy of classification.
作者
张蕾
姜宇
孙莉
ZHANG Lei;JIANG Yu;SUN Li(Division of Development and Strategic Planning,Jilin University,Changchun 130012,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2021年第5期1199-1204,共6页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:62072211).
