Special Topics in Data Mining Applications: 

Time Series Data Mining

Dr. Jessica Lin

Spring 2011



Dr. Jessica Lin 

Office: Engineering Building 4419

Phone: 703-993-4693

Email: jessica [AT] cs [DOT] gmu [DOT] edu

Office Hours: Wednesday 2-4pm


Innovation Hall 132


CS 750 or equivalent. Some programming skills required for the final project.

Textbook (optional):

Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kauffmann Publishers, March 2006. ISBN 1-55860-901-6.

Course Description:

Time series, or measurements taken over time in its traditional sense, is perhaps the most commonly encountered data type, encompassing almost every human endeavor including medicine, finance, aerospace, industry, science, etc. While time series data present special challenges to researchers due to its unique characteristics, the past decade has seen an explosion in time series data mining. This seminar provides an overview on state of the art research on mining time series data. Topics covered include data representation, similarity search, indexing, clustering, classification, anomaly detection, rule discoery, motif discovery, and visualization. Sequential pattern discovery on discrete, temporal data (web logs, customer transactions, etc). and mining of streaming time series will also be discussed.

Course Format:

The course will include lectures by the instructor, presentations from students, and class discussion. You will be asked to read research papers published in major conferences and/or journals (paper list TBA).


Grading will be based on participation, assignments, presentation(s), and a final project. You will be using Matlab in this class. Each week you are required to read two papers. Each student will present 1-2 papers in the semester.

 Participation/Attendance: 5%
 Assignments: 20%
 Presentation: 25%
 Project Proposal: 15%
 Project: 35%

Course Website