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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.
Instructor:
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
TA
Monjura Afrin Rumi
Email: mrumi [AT] gmu [DOT] edu
Classes
Tuesday
4:30-7:10pm
Innovation Hall 206
Prerequisites:
Grade of C or better in CS 310 and STAT 344
Grading
Assignments: 20%
Project: 30%
Midterm: 20%
Final: 30%
Exams
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.
Textbooks
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.)
Topics
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
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Course
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