George Mason University

Department of Computer Science

CS 584: Theory and Applications of Data Mining (Sections 004)

Fall 2024

Professor Jessica Lin


Course Description

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.

Class Time and Location

Monday 4:30-7:10pm
Art and Design Building 2003

Instructor

Dr. Jessica Lin
Email: jessica [AT] gmu [DOT] edu
Office Hours: Monday 3-4pm, Thursday 2:30-3:30pm

Teaching Assistant

TBA

Prerequisites
Grading

Programming Assignments: 40%
Quizzes: 20%
Final Exam: 30%
Class participation/Activities: 10%
          Extra credit: competition winners for homework
Grading Schema

Letter Grade Score Range
A+ >=98
A [93, 98)
A- [90, 93)
B+ [88, 90)
B [83, 88)
B- [80, 83)
C+ [76, 80)
C [72, 76)
C- [60, 72)
F <60
Assignments

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 a few hours early to avoid last minute technical difficulties.

Exams

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.

There will be 5 quizzes throughout the semester. The purpose of the quizzes is to help you stay on track of the lecture materials and assigned readings, so they are typically short (20 minutes) and easier compared to the final exam. The quiz schedule will be announced in advance. The lowest quiz grade will be dropped.

The final exam is comprehensive. While it's closed book, you are allowed to bring one letter-size, single-sided cheat sheet to the final exam.

Class Participation/Activities

You will be able to earn credit through various in-class or out-of-class activities.

Textbooks

Required: Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar (click on the link for the companion website)

Topics
Academic Standards Code (formerly Honor Code)

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. ChatGPT or other Generative-AI models may NOT be used in this course as an assistant in the assignments.


Learning Disability Accommodation

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.


GMU Common Policies

Please read the GMU Common Policies Addendum regarding policies about Academic Standards, Accommodations for Students with Disabilities, FERPA, and Title IX.