The course covers a range of topics in data mining, including distance functions, analysis of high-dimensional data, similarity search, data-stream computation, data clustering, graph mining,
spectral methods, and ranking problems.
Some of course topics will be covered from the textbook:
Leskovec, Rajaraman, and Ullman: Mining of massive datasets,
Additional reading material will be posted in the course webpage.
The syllabus will cover:
- Introduction to data mining.
- Distance functions and embeddings.
- High dimensional data and dimensionality reduction.
- Similarity search and locality-sensitive hashing
- Data-stream computation.
- Approximation algorithms for clustering problems, such as, k-means and k-median.
- Graph mining.
- Graph partitioning and spectral graph analysis.
- Link analysis and methods for ordering data.
Exercise sessions are scheduled for Thu, 2-4pm, at T2. Not every week there will be exercise sessions. We will announce in advance when there will be.
Course instructor: Aristides Gionis, email@example.com
Teaching assistants: Antonis Matakos, firstname.lastname@example.org, and Sijing Tu, email@example.com
Office hours, by appointment (email)