This talk will cover the topic of cluster analysis with an emphasis on partitioning methods. We will first introduce the topic with real-world use cases like targeted advertising and discuss the different approaches one can take to discover clusters from data. We will then zoom in on the partitioning class of clustering methods, particularly the K-means and K-medoid algorithms. With the help of toy examples, we will gain a high-level intuition about how these methods work before diving deeper to discuss the underlying algorithms and other design and implementation constructs. We will conclude the lecture by summarizing the strengths and weaknesses of this particular class of clustering methods and motivating the need for the
Satyaki Sikdar is a Ph.D. Candidate in the Department of Computer Science & Engineering at the University of Notre Dame advised by Dr. Tim Weninger. His research is at the intersection of graph theory, formal language theory, and data mining. His work has been published in IEEE ICDM, IEEE BigData, WSDM, and KAIS. He is presently the instructor of record for CSE 20110: Discrete Mathematics, a required course for Computer Science majors.
Faculty Host: Brad Solomon