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. 

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.

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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. 

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.

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This episode is an overview of the topic presented in several segments.

• Snowflake Essentials: Getting Started with Big Data in the Cloud by Frank Bell, Raj Chirumamilla, Bhaskar B. Joshi, Bjorn Lindstrom, Ruchi Soni, Sameer Videkar
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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

Erin Boyle, the Head of Data Science at Myst AI, joins us today to talk about her work with Myst AI, a time series forecasting platform and service with the objective for positively impacting sustainability. https://docs.myst.ai/docs Visit Weights and Biases at wandb.me/dataskeptic Find Better Data Faster with Nomad Data. Visit nomad-data.com

Sean Law, Principle Data Scientist, R&D at a Fortune 500 Company, comes on to talk about his creation of the STUMPY Python Library.

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