Split and rule algorithm for documents clustering in big data of research articles on Google scholar

Big data of digital documents must be ranked in online repositories as a result of the exponential rise in digital information and the user’s needs. The ranking process plays an important role in online repositories as it helps users to identify the document, what they want exactly. Various ranking techniques have been suggested on the basis of various measures, such as the number of citations of the journal article, the impact factor of the publication platform, the quality of the article, the published year of the article, bookmarks, etc. However, the current ranking algorithms often offer meaningless results due to some limitations, which suggest the potential for further development of ranking mechanisms. This paper proposes an efficient split and rule algorithm that uses both static and dynamic ranking of documents in Google scholar. The proposed algorithm uses paper citations, user input, and the clustering mechanism for document ranking. The optimized solution obtained from the proposed split and rule algorithm offers a cluster-shaped filtered search result list against the user query.