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 at TU Eindhoven joins us to discuss her work around spectral clustering and how its result could potentially cause a massive shift from the conventional neural networks. Listen to learn about her findings. Visit our website for additional show notes Thanks to our sponsor, Weights & Biases

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 algorithms and what makes it stand out from other K-means variations. He extensively discussed the working principle of the algorithm and the subtle but impactful features that enables it produce top-notch results with low computational resources. Listen to learn about this algorithm.

In today’s episode, Jason, an Assistant Professor of Statistical Science at Duke University talks about his research on K power means. K power means is a newly-developed algorithm by Jason and his team, that aims to solve the problem of local minima in classical K-means, without demanding heavy computational resources. Listen to find out the outcome of Jason's study. Click here to access additional show notes on our website! Thanks to our Sponsors:ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale. https://clear.ml Springboard Springboard offers end-to-end online data career programs that encompass data science, data analytics, data engineering, and machine learning engineering.

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.  Check out our website for extended show notes! Thanks to our Sponsors:ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale.

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 extraction using clustering techniques. Want to find out more? Listen to discover the methodologies he used for his research and the commensurate results. Visit our website for extended show notes! https://clear.ml/ ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale.

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 purely on color, in which case, k-means clustering is a good option.  Check out our website for extended show notes and images! Thanks to our Sponsors: Visit Weights and Biases mention Data Skeptic when you request a demo! & Nomad Data  In the image below, you can see the k-means clustering segmentation results for the same image with the values of 2, 4, 6, and 8 for k.

In today’s episode, Gregory Glatzer explained his machine learning project that involved the prediction of elephant movement and settlement, in a bid to limit the activities of poachers. He used two machine learning algorithms, DBSCAN and K-Means clustering at different stages of the project. Listen to learn about why these two techniques were useful and what conclusions could be drawn. Click here to see additional show notes on our website! Thanks to our sponsor, Astrato

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 overview of the topic presented in several segments.

Frank Bell, Snowflake Data Superhero, and SnowPro, joins us today to talk about his book “Snowflake Essentials: Getting Started with Big Data in the Cloud.”  Snowflake Essentials: Getting Started with Big Data in the Cloud by Frank Bell, Raj Chirumamilla, Bhaskar B. Joshi, Bjorn Lindstrom, Ruchi Soni, Sameer Videkar Snowflake Solutions Snoptimizer - Snowflake Cost, Security, and Performance Optimization - Coming Soon! Thanks to our Sponsors: Find Better Data Faster with Nomad Data. Visit nomad-data.com Visit Springboard and use promo code DATASKEPTIC to receive a $750 discount

Zack Labe, a Post-Doctoral Researcher at Colorado State University, joins us today to discuss his work “Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles.” Works Mentioned “Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles” by Zachary M. Labe, Elizabeth A. Barnes Sponsored by: Astrato and BBEdit by Bare Bones Software