Scoring based unsupervised approach to classify research papers

Platforms for publishing research papers are increasing largely that contribute to big data as their volume is humongous and are unstandardized. Classification of this huge chunk of data is one of the biggest challenges in Information Retrieval. In this paper we discuss a scoring based unsupervised learning approach to extract relevant features and classify the research papers according to their content on a two class dataset. Feature extraction is carried out by analyzing the sections of a research paper and scoring them, followed by a two level hybrid classification technique based on title and conceptual summary using graph clustering. Promising experimental results are observed for data set using our research paper classification method. We present the experimental results of our proposed algorithm for feature extraction and clustering and compare the same with different approaches reported in literature.