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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: Wed/Thurs 2-3pm
TA
TBA
Classes
Thursday
4:30-7:10pm
Robinson Hall B208
Prerequisites:
Knowlege in: statistics, probability, and linear
algebra. Some programming skill is required.
Grading
Assignments: 30%
Class Participation: 5%
Project: 35%
Midterm: 30%
Quizzes (extra credit):
up to 3%
Exams
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.
Textbooks
Required: Introduction
to Data Mining by Pang-Ning Tan, Michael
Steinbach, and Vipin Kumar
Additional
handouts and reading materials may be given in class.
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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