CS 6220: Data Mining Techniques

Instructor: Yizhou Sun


Lecture times: Wednesdays 6 - 9 PM
Lecture location: Knowles Center 002B

About the Course

This course introduces concepts, algorithms, and techniques of data mining on different types of datasets, including (1) matrix data, (2) text data, (3) set data, (4) sequence data, (5) time series, (6) graph and network, and (7) image data. The class project involves hands-on practice of mining useful knowledge from large data sets. The course is a graduate-level computer science course, which is also a good option for senior-level computer science undergraduate students interested in the field. Also, the course may attract students from other disciplines who need to understand, develop, and use data mining systems to analyze large amounts of data.

Class Schedule



*Note: all the deadlines are 11:59PM (midnight) of the due dates; No late submissions accepted!

Regrading Policy:


Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011

Recommended books for further reading:

  1. "Data Mining: The Textbook" by Charu Aggarwal (http://www.charuaggarwal.net/Data-Mining.htm)
  2. "Data Mining" by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar (http://www-users.cs.umn.edu/~kumar/dmbook/index.php)
  3. "Machine Learning" by Tom Mitchell (http://www.cs.cmu.edu/~tom/mlbook.html)
  4. "Introduction to Machine Learning" by Ethem ALPAYDIN (http://www.cmpe.boun.edu.tr/~ethem/i2ml/)
  5. "Pattern Classification" by Richard O. Duda, Peter E. Hart, David G. Stork (http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html)
  6. "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (http://www-stat.stanford.edu/~tibs/ElemStatLearn/)
  7. "Pattern Recognition and Machine Learning" by Christopher M. Bishop (http://research.microsoft.com/en-us/um/people/cmbishop/prml/))

Q & A

You are encouraged to come to the office hours of TAs and the instructor.

Peer-based Q&A via Piazza: piazza.com/northeastern/spring2016/cs6220

Academic Integrity Policy

A commitment to the principles of academic integrity is essential to the mission of Northeastern University. The promotion of independent and original scholarship ensures that students derive the most from their educational experience and their pursuit of knowledge. Academic dishonesty violates the most fundamental values of an intellectual community and undermines the achievements of the entire University.

For more information, please refer to the Academic Integrity Web page.