Machine learning

Physics

Data mining

Mathematical analysis

Artificial intelligence

Topic model

Information retrieval

Class (philosophy)

Mathematics

Computer science

Latent Dirichlet allocation

Term (time)

Quantum mechanics

Cluster analysis

Scheme (mathematics)

tf–idf

Human-centric Computing and Information Sciences

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

Research paper classification systems based on TF-IDF and LDA schemes