Rank Based Clustering For Document Retrieval From Biomedical Databases

TL;DR

This paper introduces a rank-based clustering method for biomedical document retrieval that improves the organization and relevance representation of search results from Medline and PubMed databases.

Contribution

It proposes a novel page ranking based clustering approach combined with graph tree visualization to enhance biomedical document retrieval and relativeness assessment.

Findings

Improved document clustering and relevance representation.

Enhanced visualization of document relatedness.

Better retrieval performance in biomedical databases.

Abstract

Now a day's, search engines are been most widely used for extracting information's from various resources throughout the world. Where, majority of searches lies in the field of biomedical for retrieving related documents from various biomedical databases. Currently search engines lacks in document clustering and representing relativeness level of documents extracted from the databases. In order to overcome these pitfalls a text based search engine have been developed for retrieving documents from Medline and PubMed biomedical databases. The search engine has incorporated page ranking bases clustering concept which automatically represents relativeness on clustering bases. Apart from this graph tree construction is made for representing the level of relatedness of the documents that are networked together. This advance functionality incorporation for biomedical document based search engine found to provide better results in reviewing related documents based on relativeness.