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.
Friday 10:30am-1:10pm
Horizon Hall 2016
Dr. Jessica Lin
Email: jessica [AT] gmu [DOT] edu
Office Hours: Tuesday/Thursday 1:30-2:30pm
Wenjie Xi
| Letter Grade | Score Range |
|---|---|
| A+ | >=98 |
| A | [93, 98) |
| A- | [90, 93) |
| B+ | [87, 90) |
| B | [83, 87) |
| B- | [80, 83) |
| C | [60, 80) | F | <60 |
There will be 4-5 competition-style programming assignments in Python. Competition winners will get 1% extra credit added to the final grade. You are allowed 3 days of grace period past the deadline, with 10% penalty each day. You will receive 0 credit if the homework is not submitted by then. Note that internet trouble is not a valid excuse for subbmitting late. Therefore, you should plan to submit early to avoid last minute technical difficulties.
All exams in the class 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 final exam is comprehensive.
There will be 5 quizzes throughout the semester. 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 midterm and final exams. The quiz schedule will be announced in advance. The lowest quiz grade will be dropped.
There will be various in-class or out-of-class activities.
Introduction
to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin
Kumar (click on the link for the companion website)
The GMU Academic Standards 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 Academic Standards or the CS department Honor Code is considered a violation. All assignments for this class are individual unless otherwise specified.
Students are strictly prohibited from using AI or other assistive technologies for any part of their coursework, including but not limited to: developing data structures or algorithms, generating results, writing documentation, or preparing reports for assignments and projects. For the purposes of the GMU CS Honor Code, the use of AI tools is considered equivalent to receiving assistance from “someone else” who is not the instructor or a teaching assistant.
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.
Please read the GMU Common Policies Addendum regarding policies about Academic Standards, Accommodations for Students with Disabilities, FERPA, and Title IX.