Presenter: Brian Luke, Ph.D., Senior Principle Computational Scientist, Advanced Biomedical Computational Science (ABCS) 

Clustering is an unsupervised machine learning technique that groups together similar objects (samples, individuals, etc.). Determining the similarity between objects requires a distance metric. This talk provides a brief overview of distance metrics, hierarchical (agglomerative and divisive) and non-hierarchical (K-means) clustering, and their relative strengths and weaknesses. This is a high-level introduction. This talk is also part of a complementary demo-based coding event with the Bioinformatics Training and Education Program (BTEP) Coding Club, entitled “Clustering with R and RStudio”. It is recommended, but not required, for attendees to attend this complementary event to learn the coding aspects of clustering. 

This will be a hybrid event. To register, please visit the following link: https://cbiit.webex.com/weblink/register/r69e7517c4b660e7d3843bade02caa8be 

This session will be recorded, and materials will be shared with attendees a few days after the event. 

For additional details and questions, please contact Natasha Pacheco (natasha.pacheco@nih.gov), Advanced Biomedical Computational Science group, Frederick National Laboratory for Cancer Research.