CS 739 Spatial Data Mining
Course Syllabus
Spring 2011

TEXT BOOK

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

REFERENCE BOOK

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

COURSE DESCRIPTION

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.

METHODOLOGY

Lecture and interactive problem solving.

APPRAISAL

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

GRADING

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

READING

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.

OTHER POLICIES

1. Homework:
   • All homework must be typed not hand-written and must be submitted with the given cover page (Download the cover pages from the Assignments page).
   • Homework is due exactly at the prescribed time. No late homework is accepted.
   • Turn in the paper copy of a homework assignment in class and submit the softcopy of the assignment through your UMass Online Blackboard Blackboard Learning System (BLS) account at http://boston.umassonline.net/index.cfm.
   • Any questions or complaints regarding the grading of an assignment or test must be raised within one week after the score or the graded assignment is made available (not when you pick it up).
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.

© Wei Ding, 2011, all rights reserved. This document is made available for use by the students of CS 739 at the University of Massachusetts Boston. Copying, distribution or other use of this document without express permission of the author is forbidden. You may create links to pages in this web site, but may not copy all or part of the text without permission of the author.