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Instructor: Dr. Jessica Lin Office: Engineering Building 4419 Phone:
703-993-4693 Email:
jessica [AT] cs [DOT] gmu [DOT] edu Office Hours: Wednesday 2-4pm Classes Thursdays
7:20-10:00pm
Innovation Hall 132 Prerequisite: 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
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
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