George Mason University

Department of Computer Science

CS 484: Data Mining

Fall 2019

Professor Jessica Lin

Course Description

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

Class Time and Location

Tuesday/Thursday, 12-1:15pm
Sandbridge Hall 107

Instructor

Dr. Jessica Lin
Office: Engineering Building 4419
Phone: 703-993-4693
Email: jessica [AT] gmu [DOT] edu
Office Hours: Tuesday/Thursday 10:30-11:30am

Teaching Assistant

Li Zhang
Office Hours: TBA
Office: TBA

Prerequisites

• Formally: Grade of C or better in CS 310 and STAT 344


• More specifically: Programming experience in Python preferred. Java or C will work as well, but the assignments will use the Python framework. Students should be familiar with basic probability and statistics concepts, and linear algebra. Please expect lots of programming in all the assignments and class projects.

• 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)

Grading

Assignments: 40%

Project: 35%

Exam: 25%

          Extra credit possible (participation, quizzes, competition winners for Homework)

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.

Project

There will be one team project in the semester. The project grade consists of project proposal (including project pitch, 5% total of overall course grade), presentation (10%), and project report and code (20%).

Textbooks

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

Topics

• Ch.1: Introduction
• Ch.2: Data
• Ch.3: Classification
• Ch.4: Classification: Alternative Techniques
• Ch.5: Association Analysis: Basic Concepts and Algorithms
• Ch.6: Association Analysis: Advanced Concepts
• Ch.7: Cluster Analysis: Basic Concepts and Algorithms
• Ch.8: Cluster Analysis: Additional Issues and Algorithms
• Ch.9: Anomaly Detection

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.