George Mason University
  Department of Computer Science

CS 750 - Theory and Applications of Data Mining

Dr. Jessica Lin

Spring 2012



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:  Wed/Thurs  2-3pm




Robinson Hall B208


Knowlege in: statistics, probability, and linear algebra. Some programming skill is required.


Assignments: 30%
Class Participation: 5%
Project: 35%

Midterm: 30%

 Quizzes (extra credit): up to 3%


You may earn up to 3% extra credit on quizzes, which will be given in the beginning of the class. They may or may not be announced in advance. There will be a midterm exam covering lectures and readings (in class, closed book). Exams must be taken at the scheduled time and place. Missed exams cannot be made up.

Honor Code Statement

Please be familiar with the GMU Honor Code. Any deviation from this is considered an Honor Code violation. All assignments for this class are individual unless otherwise specified.


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

 Additional handouts and reading materials may be given in class.

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