k-Means Clustering

Clustering helps us find structure in unlabeled data — and this short season is a deep dive into one of the most popular algorithms: *k*-means. With clear explainers and guest interviews, the episodes cover how it works, where it struggles, and how to use it well. Applications span domains like marketing, vision, and compression, illustrating why this classic method still holds up in modern practice.

Episodes

• Quantum K-Means — In this episode, we interview Jonas Landman, a Postdoc candidate at the University of Edinburg. Jonas discusses his study around quantum learning where he attempted to recreate the conventional k-mean
• K-Means in Practice — K-means is widely used in real-life business problems. In this episode, Mujtaba Anwer, a researcher and Data Scientist walks us through some use cases of k-means. He also spoke extensively on how to p
• Fair Hierarchical Clustering — Building a fair machine learning model has become a critical consideration in today’s world. In this episode, we speak with Anshuman Chabra, a Ph.D. candidate in Computer Networks. Chhabra joins us to
• Matrix Factorization For k-Means — Many people know K-means clustering as a powerful clustering technique but not all listeners will be as familiar with spectral clustering. In today’s episode, Sibylle Hess from the Data Mining group a
• Breathing K-Means — In this episode, we speak with Bernd Fritzke, a proficient financial expert and a Data Science researcher on his recent research - the breathing K-means algorithm. Bernd discussed the perks of the alg
• Explainable K-Means — In this episode, Kyle interviews Lucas Murtinho about the paper "Shallow decision treees for explainable k-means clustering" about the use of decision trees to help explain the clustering partitions.
• Customer Clustering — Have you ever wondered how you can use clustering to extract meaningful insight from a time-series single-feature data? In today’s episode, Ehsan speaks about his recent research on actionable feature
• k-means Image Segmentation — Linh Da joins us to explore how image segmentation can be done using k-means clustering. Image segmentation involves dividing an image into a distinct set of segments. One such approach is to do this
• k-means clustering — Welcome to our new season, Data Skeptic: k-means clustering. Each week will feature an interview or discussion related to this classic algorithm, it's use cases, and analysis. This episode is an overv
• Quantum Computing — In this week's episode, Scott Aaronson, a professor at the University of Texas at Austin, explains what a quantum computer is, various possible applications, the types of problems they are good at sol