Clustering in Data Mining and Machine Learning - Single Linkage Hierarchical Clustering | Step-by-Step Solved Example | Agglomerative Clustering

Single Linkage Hierarchical Clustering | Agglomerative Method | Solved Numerical Example | Machine Learning by Vidya Mahesh Huddar Single inkage CLustering: https://youtu.be/-oPyDp1IpV4 Complete inkage CLustering: https://youtu.be/ADRRbUSoIAc In this video, we explain Single Linkage Hierarchical Clustering using the Agglomerative Method with a complete solved numerical example. Step-by-step, we: • Start with initial clusters (each data point as a separate cluster) • Calculate Euclidean distances • Find the minimum distance between clusters • Merge clusters using the Single Linkage (minimum distance) criterion • Recompute distance matrices after every merge • Construct the final Dendrogram The data points are: Point 1 → (2, 6) Point 2 → (3, 4) Point 3 → (3, 7) Point 4 → (6, 2) Point 5 → (7, 3) This video clearly demonstrates how clusters are merged in the following order: d(1,3) = 1 d(4,5) = 1.41 d(C13, C2) = 2.0 Final merge at distance 5.1 ******************************** Follow Us on: 1. Blog / Website: https://www.vtupulse.com/ 2. Download Final Year Project Source Code: https://vtupulse.com/download-final-year-projects/ 3. Like Facebook Page: https://www.facebook.com/VTUPulse 4. Follow us on Instagram: https://www.instagram.com/vtupulse/ 5. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates