Clustering of clinical documents is the major research area in the field of machine learning and artificial intelligence which aims to acquaint some type of association with the information that helps to highlight huge examples and patterns. The rich corpus of clinical notes consists of several unprocessed data which needs to be mined with appropriate technique to improvise the existing healthcare system. Biomedical information mining is a research strategy to recover, break down and analyze clinical information from a collection of medicinal records. This paper presents a novel approach that utilizes Non-Negative Matrix Factorization Clustering approach to mine the medication names based on age of the patients. Pharmaceutical data from clinical notes is regularly communicated with prescription names and other medication information which needs to be mined based on the similarity between documents so that more accurate extraction of similarity could be accomplished. Even in the wake of being an exceptionally effective solution, clustering is yet not deployed in the major search engines. The basic issue with it is to determine a fast and accurate cluster values even after reducing the complexity of the technique. This paper presents an enhanced multi-viewpoint similarity measure that utilizes many distinct viewpoints to measure similarity between documents so that more accurate extraction of similarity could be accomplished. © 2017 Oriental Scientific Publishing Company.