Clustering: Graph-based Methods
Ramin Javadi - Associate Professor, Isfahan University of Technology
Thu, 14-April-2022 / 18:00 / Link:
Video Slides Poster


Clustering as a machine learning task is defined as an unsupervised procedure of partitioning data points into a number of groups such that the points in each group have high similarity while the points in different groups have small similarity. Finding communities with similar traits is the main goal of clustering which has received remarkable attention and tremendous progress in recent decades due to its widespread applications in different areas such as community detection, image and signal processing, pattern recognition and computer vision. In this talk, we review some important and classical methods of clustering with the main focus on the methods using tools from graph theory and linear algebra.


Ramin Javadi received his PhD from Sharif University of Technology in 2011. He is currently an associate professor of mathematics at Isfahan University of Technology. His research interests touch on graph theory, combinatorics, extremal combinatorics, algebraic graph theory, geometric analysis on graphs, algorithms, computational complexity, parameterized complexity and their interconnections.