Files
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Course
Machine Learning
Description
Clustering methods demonstrated their transformative potential across various industries through image segmentation, anomaly detection, bioinformatics, and customer segmentation. The presentation explores 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 presentation highlights how these methods have evolved to address complex, high-dimensional data problems. The presentation provides insights into how K-means remains a versatile tool for partitioning data in linear spaces. It delves 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 presentation offers a comprehensive understanding of these algorithms and equips aspiring data scientists and industry professionals with the tools to harness the power of clustering for impactful, data-driven decisions.
Publication Date
Spring 3-31-2025
Document Type
Presentation
City
San Jose
Disciplines
Computational Engineering | Computer Engineering
Rights
To cite this presentation: Pendyala, V.S. (2025)
“Unveiling the Transformative Power of Unsupervised
machine learning through Clustering”. IEEE Computer
Society, Kitchener-Waterloo Chapter Technical Talk,
March 31, 2025
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Pendyala, Vishnu S., "Unveiling the Transformative Power of Unsupervised machine learning through Clustering" (2025). Open Educational Resources. 6.
https://scholarworks.sjsu.edu/oer/6
