George Mason University
  Department of Computer Science

CS 584 - Theory and Applications of Data Mining

Dr. Jessica Lin

Spring 2016



Course Description

Concepts and techniques in data mining and multidisciplinary applications. Topics include databases; data cleaning and transformation; concept description; association and correlation rules; data classification and predictive modeling; performance analysis and scalability; data mining in advanced database systems, including text, audio, and images; and emerging themes and future challenges.


Dr. Jessica Lin 

Office: Engineering Building 4419
Phone: 703-993-4693
Email: jessica [AT] cs [DOT] gmu [DOT] edu
Office Hours:  Tuesday 2-4pm or by appointment


 Monjura Afrin Rumi
 Email: mrumi [AT] gmu [DOT] edu


Innovation Hall 206


Grade of C or better in CS 310 and STAT 344


Assignments: 20%
Project: 30%

Midterm: 20%
Final: 30%


There will be a midterm exam covering lectures and readings (in class, closed book). The final exam is comprehensive. Exams must be taken at the scheduled time and place, unless prior arrangement has been made with the instructor. Missed exams cannot be made up.

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 Accommodations

If you have a documented learning disability or other condition which may affect academic performance, please make sure this documentation is on file with the Office of Disability Services and then discuss with the professor about accommodations.


  Required: Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar

 Recommended: Data Mining and Analysis by Mohammed Zaki (Here is the online pdf version.)

Ch.1: Introduction
Ch.2: Data
Ch.4: Classification
Ch.5: Classification: Alternative Techniques
Ch.6: Association Analysis: Basic Concepts and Algorithms
Ch.7: Association Analysis: Advanced Concepts
Ch.8: Cluster Analysis: Basic Concepts and Algorithms
Ch.9: Cluster Analysis: Additional Issues and Algorithms
Ch.10: Anomaly Detection

Course Website