George Mason University

Department of Computer Science

CS 484: Data Mining (Sections 001)

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.

Course Learning Outcomes

• The ability to apply computing principles, probability and statistics relevant to the data mining discipline to analyze data.
• A thorough understanding of model programming with data mining tools, algorithms for estimation, prediction, and pattern discovery.
• The ability to analyze a problem, identifying and defining the computing requirements appropriate to its solution: data collection and preparation, functional requirements, selection of models and prediction algorithms, software, and performance evaluation.
• The ability to understand performance metrics used in the data mining field to interpret the results of applying an algorithm or model, to compare methods and to reach conclusions about data.
• The ability to communicate effectively to an audience the steps and results followed in solving a data mining problem (through a term project)

Class Time and Location

Tuesday/Thursday 12:00-1:15pm
Horizon Hall 2016

Instructor

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

Teaching Assistant

TBA

Prerequisites

• Formally: Grade of C or better in CS 310 (Data Structures) and STAT 344 (Probability and Statistics) or equivalent.
  


• More specifically: Programming experience in Python, or willing to learn. Experience in Java or C++ will work as well, but the assignments will use the Python framework. Students should be familiar with basic probability and statistics concepts, and linear algebra. Please expect lots of programming in the assignments.

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-	[68, 72)
D	[60, 68)
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

• Ch.1: Introduction
• Ch.2: Data
• Ch.3: Classification
• Ch.4: Classification: Alternative Techniques
• Ch.5: Association Analysis: Basic Concepts and Algorithms
• Ch.6: Association Analysis: Advanced Concepts
• Ch.7: Cluster Analysis: Basic Concepts and Algorithms
• Ch.8: Cluster Analysis: Additional Issues and Algorithms
• Ch.9: Anomaly Detection
• Recommendation Systems

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.

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