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In this video, we introduce K-Medoids clustering, an alternative approach to K-Means that offers robustness against outliers. We begin by taking you through the core concepts visually and using a hands-on example with a small 2D dataset. Witness how K-Medoids works its magic as we manually walk you through the process.
We also show you how to put theory into practice using Python. Witness the step-by-step implementation of K-Medoids clustering, and see how it compares to the more widely known K-Means method.
Discover the intuitive difference between medoids and means, and how this distinction can lead to more resilient cluster assignments. Gain insights into the advantages of K-Medoids, especially in scenarios where outliers can disturb on traditional clustering methods.
This video is a perfect starting point, if you’re ready to grasp the essentials of K-Medoids clustering.
Happy Learning!
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