CS 484: Data Mining

George Mason University

Department of Computer Science

CS 484: Data Mining (In Person)

Fall 2022

Professor Jessica Lin

Course Description: Basic principles and methods for data analysis and knowledge discovery. Emphasizes developing basic skills for modeling and prediction and performance evaluation. Topics include system design; data quality, preprocessing, and association; event classification; clustering; biometrics; business intelligence; and mining complex types of data.
Class Time and Location: Tuesday/Thursday 12:00-1:15pm
Music/Theater Building 1005
Instructor: Dr. Jessica Lin
Email: jessica [AT] gmu [DOT] edu
Office Hours: TBA
Teaching Assistant: Bikram Adhikari
Office Hours: TBA
Office: 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 preferred. 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 all the assignments and class project.

Course 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)

Grading: Programming Assignments: 50%; Quizzes: 15%; Final Exam: 30%; Class participation/Activities: 5%
Extra credit: competition winners for homework

Assignments

There will be 4-5 competition-style programming assignments. The preferred programming language is Python. Competition winners will get 1% extra credit added to the final grade. No late submission will be accepted.

Exams

There will be quizzes throughout the semester covering lectures and readings, and one final exam. The final exam is comprehensive. All exams are closed-book, and they must be taken at the scheduled time and place, unless prior arrangement has been made with the instructor. Missed exams cannot be made up. The lowest quiz grade will be dropped.

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

Honor Code Statement

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.

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.

Course Website