Biomedical Document Clustering

Biomedical document clustering is a technique used to group similar biomedical documents into clusters based on their content. Biomedical documents include research papers, medical records, clinical trial reports, and other literature that contain valuable information on biological and medical topics. Clustering the documents in the biomedical domain can help researchers identify relationships between different concepts, extract useful information, and discover new knowledge. It can also be used for tasks such as identifying similar clinical cases, discovering new drug targets, and finding commonalities between different diseases. While performing clustering careful consideration must be given to the choice of clustering algorithm, feature selection, and evaluation metrics to ensure accurate and meaningful results. This chapter provides a comprehensive overview of the clustering process and outlines the necessary steps required to cluster biomedical documents effectively. One critical aspect of this process is the representation of textual data in vector form, which  appears prominentely  in this chapter. For clustering, a similarity measure must be applied to determine the relatedness of documents to one another, the chapter focuses on different similarity measures that can be used to find similarity between textual entities. The chapter also explores clustering algorithms, with a specific focus on K-means clustering. Additionally, the chapter presents clustering experiments conducted on a small dataset that applies various word/document representations with K-means algorithm. By doing so, the chapter aims to provide a practical understanding of the concepts discussed throughout the chapter.