Clustering divides data objects into groups to minimize the variation within each group, while maximizing the different between groups. This technique is widely used in data mining and other areas of computer science. K-means is a partitional clustering algorithm that produces a fixed number of clusters through an iterative process. The relative simplicity, flexibility, and efficiency of the K-means algorithm makes it a popular choice for a variety of clustering applications.
Rachel Krohn is currently a Ph.D. candidate in the Computer Science and Engineering Department at the University of Notre Dame. Previously, she attended the South Dakota School of Mines and Technology, where she earned BS degrees in Computer Science and Mechanical Engineering, and a MS in Computational Science and Robotics. Her research is focused on social media, specifically the dynamics of information diffusion and adoption via online platforms. Beyond research, Rachel is passionate about teaching, and has taught courses at both SDSM&T and Notre Dame. She is also a long-time participant in the International Collegiate Programming Contest, first as a contestant, but more recently as a coach and judge.
Faculty host: Michael Nowak