期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2015
卷号：8
期号：6
页码：95-106
DOI：10.14257/ijhit.2015.8.6.10
出版社：SERSC
摘要：A lot of time of the users is consumed in searching appropriate papers related to the desired topic. It takes time to look through the paper also. In this paper, a hybrid method is introduced to classify research papers. This algorithm is designed to classify all research papers at the time of uploading in the repository. Hence it becomes easy to explore appropriate paper on a specific topic in minimum time. A data set has generated with research papers on different topics like natural language processing, machine learning, etc. The proposed algorithm passes the most frequent items fetched from the training data set to k-nearest neighbor method instead of the whole data set, to make clusters. The performance of the proposed method is compared with traditional KNN method which results the accuracy, improved by the factor of 7.46%.
关键词：classification; Frequent term mining; KNN; Text mining
