Concepts and techniques in data mining and multidisciplinary
applications. Topics include databases; data cleaning and
transformation; concept description; association and correlation rules;
data classification and predictive modeling; performance analysis and
scalability; data mining in advanced database systems, including text,
audio, and images; and emerging themes and future challenges.
Innovation Hall 132
Dr. Jessica Lin
Office: Engineering Building 4419
Email: jessica [AT] gmu [DOT] edu
Office Hours: Tuesday 2-4pm
Office Hours: TBA
There will be one exam covering lectures and readings (in class, closed book). The exam must be taken at the scheduled time and place, unless prior arrangement has been made with the instructor. Missed exam cannot be made up.
There will be one team project in the semester. The project
consists of project proposal (including project pitch, 5% total of
overall course grade), presentation (10%), and project report and code
to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin
Kumar (click on the link for the companion website)
The GMU Honor Code is in effect at all times. In addition, the CS Department has further honor code policies regarding programming projects, which are detailed here. Any deviation from the GMU or the CS department Honor Code is considered an Honor Code violation. All assignments for this class are individual unless otherwise specified.
If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and then discuss with the professor about accommodations.