Concepts and techniques in data mining and multidisciplinary
applications. Topics include data cleaning and transformation;
classification and predictive modeling; clustering; association
analysis; performance analysis and scalability; data mining in
advanced database systems, including text, audio, and images; and emerging themes and future
challenges. Students will gain hands-on experience and learn how to
implement and apply various data mining algorithms.
Section 004: Monday 4:30-7:10pm
Art and Design Building 2003
Section 006: Thursday 7:20pm-10:00pm
Exploratory Hall L003
Dr. Jessica Lin
Email: jessica [AT] gmu [DOT] edu
Office Hours: Monday/Thursday 3-4pm
Madhukar Vongala
There will be 4-5 competition-style programming assignments in Python. Competition winners will get 1% extra credit added to the final grade.
There will be quizzes throughout the semester covering lectures and readings, and one final exam. The purpose of the quizzes is to help you stay on track of the lecture materials, so they are typically short and easier compared to the final exam. The final exam is comprehensive. All exams are closed-book, and they must be taken at the scheduled time, unless prior arrangement has been made with the instructor. Missed exams cannot be made up. The lowest quiz grade will be dropped.
You will be able to earn class participation credit through
in-class activities.
Required: Introduction
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. Some examples can be found 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. ChatGPT or other Generative-AI models may NOT be used in this course as an assistant in the assignments.
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.