An Introduction to Clustering | Unsupervised Learning, K-Means & Hierarchical Clustering

Описание: This video provides a complete introduction to clustering in unsupervised learning, following a university-level lecture structure.

We start with the fundamentals of clustering and similarity measures, then move on to K-Means clustering in detail, including its algorithm, convergence criteria, strengths, and weaknesses. The lecture also covers hierarchical clustering (divisive and agglomerative), cluster evaluation metrics such as SSE, and applications in computer vision like image segmentation and bag-of-words models.

📌 Topics covered in this lecture:
What is clustering and why it matters
Similarity and distance measures (Euclidean, Minkowski)
Cluster evaluation: cohesion, separation, SSE
How many clusters should we choose?
K-Means clustering algorithm (step-by-step)
Convergence criteria and limitations of K-Means
Sensitivity to initialization and outliers
Why K-Means fails on non-spherical clusters
Hierarchical clustering: divisive vs agglomerative
Dendrograms and biological taxonomy examples
Applications in computer vision and neuroscience

🎓 This video is ideal for:
Machine Learning & Data Mining students
Exam and course revision
Researchers new to clustering
Anyone learning unsupervised learning fundamentals

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