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CS 739 Spatial Data Mining
Course Syllabus
Spring 2011

Instructor: Dr. Wei Ding
Office: S-3-167, Science Building , 3rd floor
Class Schedule:T TH 5:30 - 6:45 PM,  Web Lab (Science Building, S-3-28) McCormack M01-0616
Office Hours: T TH 4:30 - 5:30 PM
TA: Mr. Yang Mu
Information: E-mail:
Office Hours: KDLab S-3-158A
Tuesday 4.00 PM - 5:00 PM
Thursday 3.00 PM - 4:00 PM
or by appointment


Introduction to Data Mining Introduction to Data Mining
by Pang-Ning Tan and Michael Steinbach and Vipin Kumar
Addison Wesley
ISBN: 0-321-321136-7 769 pages
Published: 2006


Pattern Recognition and Machine Learning Pattern Recognition and Machine Learning
by Christopher M. Bishop
ISBN: 978-0387310732 738 pages
Published: 2007 (2nd printing edition)


This course treats a specific advanced topic of current research interest in the area of handling spatial, temporal, and spatio‐temporal data. Major topics include data mining and machine learning techniques on clustering, association analysis, and classification. In addition, students will learn how to use popular data mining tools and how to implement applications in geosecience. The class will expose students to interdisciplinary research on spatial data mining and current industrial practices in handling spatio‐temporal data.


Lecture and interactive problem solving.


Participation: 10% of the total
Term Project: 60% of the total
Examination: 30% of the total


91+ = A; 89+ = A-;
87+ = B+; 83+ = B; 80+ = B-;
77+ = C+; 73+ = C; 70+ = C-;
67+ = D+; 63+ = D; 60+ = D-;
0+ = F;


We will read from the recommended text book, various sources on the web, and slides that will be made available on the web site. The schedule for the readings are given on the schedule web page.


  1. Homework:
  2. Providing answers for any examination when not specifically authorized by the instructor to do so, or, informing any person or persons of the contents of any examination prior to the time the examination is given is considered cheating.
  3. Penalty for cheating will be extremely severe. Use your best judgment. If you are not sure about certain activities, consult the instructor. Standard academic honesty procedure will be followed for cheating and active cheating automatically results F in the final grade. Please check University Policy on Academic Standards and Cheating for additional information.
  4. You are expected to come fully prepared to every class.
  5. No incomplete grade under nearly all situations.
  6. There is no formal attendance policy. However, you are responsible for everything discussed in class. You may receive a zero for lack of participation.
  7. Pay very careful attention to your email correspondence. It reflects on your communication skills. Avoid using non-standard English such as "how r u?" in your email message. In addition, I recommend you put the class number and a brief summary of your question in your email subject. For example,

    Subject: CS739 A question on spatial association rules.

  8. I immediately discard anonymous emails.
  9. The ringing, beeping, buzzing of cell phones, watches, and/or pagers during class time is extremely rude and disruptive to your fellow students and to the class flow. Please turn off all cell phones, watches, and pagers prior to the start of class.

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