George Mason University

Department of Computer Science

CS 584: Theory and Applications of Data Mining

Fall 2018

Professor Jessica Lin


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.

Class Time and Location

Tuesday 4:30-7:10pm
Art and Design Building L008

Instructor

Dr. Jessica Lin
Office: Engineering Building 4419
Phone: 703-993-4693
Email: jessica [AT] gmu [DOT] edu
Office Hours: Tuesday 1:30-3:30pm

Teaching Assistant

Priya Mani
Email: pmani [AT] gmu [DOT] edu
Office Hours: Monday/Thursday 4-5pm
Office: Engineering Building 4456

Prerequisites
Grading

Assignments: 40%
Project: 45%
Exam: 15%

Exam

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

Textbooks

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

Topics
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.

Tentative Schedule

Week
Date
Topic
Assigned
Due
Note
1
8/28
Introduction (Ch. 1)
HW0


2
9/4
Classification (Ch. 3-4)

HW0

3
9/11
Classification



4
9/18
Classification



5
9/25
Classification
HW2
HW1 (9/22)

6
10/2
Classification / Clustering (Ch. 7-8)



7
10/9
No Class



8
10/16
Clustering
HW3
HW2 (10/14)

9
10/23
Clustering



10
10/30
Association Analysis

Project Proposal

11
11/6
Association Analysis

HW3 (11/9)

12
11/13
Exam
HW4


13
11/20
Anomaly Detection

HW4

14
11/27
Project presentation



15
12/4
Project presentation



16
12/11


Project report