CS 484 - Data Mining

Dr. Jessica Lin

Fall 2015

Course Description

Basic principles and methods for data analysis and knowledge discovery. Emphasizes developing basic skills for modeling and prediction, on one side, and performance evaluation, on the other. Topics include system design; data quality, preprocessing, and association; event classification; clustering; biometrics; business intelligence; and mining complex types of data.

Instructor:

Dr. Jessica Lin 

Office: Engineering Building 4419
Phone: 703-993-4693
Email: jessica [AT] cs [DOT] gmu [DOT] edu
Office Hours:  TBA

TA

Classes

Tuesday/Thursday
12-1:15pm
Art & Design Building 2026

Course Outcomes

• The ability to apply computing principles, probability and statistics relevant to the data mining discipline to analyze data.
• A thorough understanding of model programming with data mining tools, algorithms for estimation, prediction, and pattern discovery.
• The ability to analyze a problem, identifying and defining the computing requirements appropriate to its solution: data collection and preparation, functional requirements, selection of models and prediction algorithms, software, and performance evaluation.
• The ability to understand performance metrics used in the data mining field to interpret the results of applying an algorithm or model, to compare methods and to reach conclusions about data.
• The ability to communicate effectively to an audience the steps and results followed in solving a data mining problem (through a term project)

Prerequisites:

Grading

Assignments: 20%
Project: 20%
Midterms: 30%
Final: 30%

Exams

There will be two midterm exams and one final exam covering lectures and readings. All exams will be 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.

Textbooks

 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