Data Mining - Spring 2025

Explore fundamental concepts and advanced techniques in data mining through this comprehensive lecture series from the University of Utah. Master essential algorithms and methodologies including clustering techniques like k-means, hierarchical clustering, and spectral clustering, while diving deep into dimensionality reduction methods such as PCA and random projections. Learn about similarity measures, locality-sensitive hashing (LSH), and min-hashing for efficient data processing. Discover graph analysis techniques including PageRank, Markov chains, community detection, and graph embeddings. Understand privacy-preserving data mining through differential privacy concepts, and explore outlier detection methods. Gain expertise in streaming data processing, approximate nearest neighbor (ANN) algorithms, word embeddings, and metric distances. Study advanced topics like distances for distributions and practical applications of data mining algorithms across various domains.

Syllabus

Data Mining Lecture L25 - Graph Embeddings
Data Mining Lecture 24 - Graph Communities
Data Mining Lecture 23 - PageRank
Data Mining Lecture 22 - Markov Chains (for Graphs)
Data Mining Lecture L21 - Differential Privacy
Data Mining Lecture 21- Privacy
Data Mining Lecture 20 - Outliers
Data Mining Lecture 17 - PCA
Data Mining Lecture L16 - Random Projections
Data Mining Lecture 14 - Streaming - Intro
Data Mining Lecture 13 - Spectral Clustering
Data Mining Lecture 12 - k-Means
Data Mining Lecture 11 - Hierarchical Clustering
Data Mining Lecture L10 - Distances for Distributions
Data Mining Lecture 9 - ANN
Data Mining Lecture 8 - Word Embeddings
Data Mining Lecture 7 - Metric Distances
Data Mining Lecture 6 - LSH
Data Mining Lecture 5 - Min Hashing
Data Mining Lecture - L4
Data Mining Lecture - L3
Data Mining Lecture 2
Data Mining Lecture 1