Clustering methods demonstrated their transformative potential across various industries through image segmentation, anomaly detection, bioinformatics, and customer segmentation. In this talk, the speaker will explore these techniques in unsupervised machine learning, focusing on foundational clustering algorithms such as K-means, Hierarchical Clustering, and DBSCAN. Through an in-depth analysis of their underlying principles and computational intricacies, the speaker will highlight how these methods have evolved to address complex, high-dimensional data problems. Attendees will learn how K-means remains a versatile tool for partitioning data in linear spaces. The talk will delve into Hierarchical Clustering's unique approach to building dendrograms and capturing multi-scale data relationships and how DBSCAN's density-based framework reveals clusters amidst noise, making it ideal for discovering patterns in irregular, real-world datasets. The session offers a comprehensive understanding of these algorithms. It equips aspiring data scientists and industry professionals with the tools to harness the power of clustering for impactful, data-driven decisions. Co-sponsored by: ANK Zaman Speaker(s): Vishnu Pendyala Virtual: https://events.vtools.ieee.org/m/476923