Download RSCHI 226, Tel: 993-3615, E

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Please indicate: New____X___
Modify_______
Delete_______
Department/Unit:____GGS_____________ Course Subject/Number:_______EOS 787 (GGS* 787)_________
Submitted by:_____________Ruixin Yang_________ Ext:___33615________ Email:[email protected]___
Course Title:___
Scientific Data Mining for Geoinformatics ______________________
Effective Term (New/Modified Courses only): __S2009_____
Credit Hours: (Fixed) _3__
(Var.) ______ to ______
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_____ Satisfactory/No Credit only
_____ Special graduate (A, B, C, etc. + IP)
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2.____ LAB=Lab RCT=Recitation (second code used only for courses with Lab or Rct component)
Prereq _X_ Coreq ___ (Check one):__ competency in programming or permission of instructor.___
__________________________________________________________________________________________
Note: Modified courses - review prereq or coreq for necessary changes; Deleted courses - review other courses to correct prereqs that list the deleted course.
Description of Modification (for modified courses):_______
Special Instructions (major/college/class code restrictions, if needed):__* all courses in GGS dept. will soon propose to
have the prefix GGS to eliminate overlaps and X-listings, per our merger/reorganization of ESGS and GEOG
Department/Unit Approval Signature:_________________________________________ Date: _____________
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EOS/GGS 787, Scientific Data Mining for Geoinformatics
Instructor: Ruixin Yang
RSCHI 226, Tel: 993-3615, E-mail: [email protected]
http://yang.gmu.edu/eos6xx
Time & Place: Thursdays 7:20-10:00pm, Innovation Hall 330
Office Hours: Thursdays 5:00 pm-7:00pm or by appointments
Text:
1. Han, Jiawei and Micheline Kamber, 2006, “Data Mining: Concepts and Techniques,” Second Edition,
Morgan Kaufmann, San Francisco, Calif. (ISBN: 9781558609013).
2. scientific data mining papers in geosciences and geoinformatics
Short Description: This course covers specialized data mining algorithms, geoscience data models, and data
information systems. The emphasis of this course is on domain specific data mining algorithms suitable for
spatial data and spatio-temporal data with geoscience and geoinformatics applications. Real geoscience data
mining applications will be introduced in details.
Class Email List: [email protected]
References Notes
Prerequisite: competency in programming at the level of CSI601-607 or permission of instructor.
Tentative Course Content:
Week 1:
Week 2:
Week 3:
Week 4:
Week 5:
Week 6:
Week 7:
Week 8:
Week 9:
Week 10:
Week 11:
Week 12:
Week 13:
Week 14:
Introduction to Data Mining
Mining Association Rules
Classification
Clustering Basic
Density-Based Clustering
Introduction to Spatial and Spatio-Temporal Data
Data Formats in Geosciences
Data Information Systems in Geosciences
Content-Based Geoscience Data Search
Geoscience Data Mining Applications I: Mining Climate Indices
Mining Spatial and Temporal Data
Geoscience Data Mining Applications II: Mining for Severe Weather Events
Geoscience Data Mining Applications III: Mining for Rapid Intensification of Tropical Cyclones
Student Project Presentation.
Grading: Assignments: Assignments: 30%; Mid-term: 30%; Project: 40%
Relation to other courses
The following courses are identified by the CDS curriculum committee for potential overlaps. The findings of
the relations are as follows:
CS 401.
Course PI Yang searched the current GMU catalog and the CS web site for course offered from Spring 2006 to Spring 2008 and did
not find any information on this course. This is an undergraduate course number so there is no conflict with a graduate level offering.
CS course list in current GMU catalog (undergraduate courses):
391 Advanced Programming Lab (1:0:1) Corequisite: grade of C or better in CS 310, and permission of
instructor. Programming-intensive lab course. Students refine problem-solving and programming skills while
gaining experience in teamwork. Focuses on data structures, recursion, backtracking, dynamic programming,
and debugging. Central focus is applying familiar and new algorithms and data structures to novel
circumstances.
421/SWE 421 Software Requirements and Design Modeling (3:3:0) Prerequisite: CS 211. An introduction
to concepts, methods, and tools for the creation of large-scale software systems. Methods, tools, notations, and
validation techniques to analyze, specify, prototype, and maintain software requirements. Introduction to objectoriented requirements modeling, including use of case modeling, static modeling, and dynamic modeling using
the Unified Modeling Language (UML) notation. Concepts and methods for the design of large-scale software
systems. Fundamental design concepts and design notations are introduced. A study of object-oriented analysis
and design modeling using the UML notation. Students participate in a group project on software requirements,
specification, and object-oriented software design.
IT 750
Description in current GMU catalog:
750/CS 750 Theory and Applications of Data Mining (3:3:0) Prerequisite: CS 681, 687, or 688; or permission of instructor.
Concepts and techniques in data mining and their multidisciplinary applications. Topics include databases; data cleaning and
transformation; concept description; association and correlation rules; data classification and predictive modeling; performance
analysis and scalability; data mining in advanced database systems including text, audio and images; and emerging themes and future
challenges. Term project and topical review.
This course is for students in CS only and covers mainly data mining with data in DBMS (database management systems). The only
overlap I can identify is highlighted above. There are many variations on the topic. My emphasis is on the applications and
theory/algorithms related to the applications.
CSI 772
Again, I did not find any information on this course in GMU catalog and the CDS Class Website. Nevertheless, based on input from
Igor, there is not much overlap with the modified CSI 654. I also found another CSI course, CSI 777 with the word “mining,” but I
think the main contents in the two courses are very different.
777 Principles of Knowledge Mining (3:3:0) Prerequisites: INFS 614 or equivalent, or permission of instructor. Principles and
methods for synthesizing task-oriented knowledge from computer data and prior knowledge, and presenting it in human-oriented forms
such as symbolic descriptions, natural language-like representations, and graphical forms. Topics include fundamental concepts of
knowledge mining; methods for target data generation and optimization; statistical and symbolic approaches; knowledge representation
and visualization; and new developments such as inductive databases, knowledge generation languages, and knowledge scouts.
CDS 401:
401 Scientific Data Mining (3:3:0) Prerequisite: CDS 302. Data mining techniques from statistics, machine
learning, and visualization to scientific knowledge discovery. Students will be given a set of case studies and
projects to test their understanding of this field and provide a foundation for future applications in their careers.
This is my major concern before the modification proposal and I discussed the course with Dr. Kirk Bourne. Kirk agreed that there is
no problem with his undergraduate scientific data mining course.