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George Mason University – Graduate Council                                                      Graduate Course Approval F...
George Mason University                                               Graduate Course Coordination Form

Approval from ...
EOS/GGS 787, Scientific Data Mining for Geoinformatics
                                       Instructor: Ruixin Yang
Relation to other courses
The following courses are identified by the CDS curriculum committee for potential overlaps. The...
This is my major concern before the modification proposal and I discussed the course with Dr. Kirk Bourne. Kirk agreed tha...
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EOS 787 approved


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EOS 787 approved

  1. 1. George Mason University – Graduate Council Graduate Course Approval Form All courses numbered 500 or above must be submitted to the Graduate Council for final approval after approval by the sponsoring College, School or Institute. Graduate Council requires submission of this form for a new course or any change to existing courses. For a new course, please attach a copy of the syllabus and catalog description (with catalog credit format, e.g. 3:2:1). The designated representative of the College, School or Institute should forward the form along with the syllabus and catalog description, if required, as an email attachment (in one file) to the secretary of the Graduate Council. A printed copy of the form with signatures and the attachments should be brought to the Graduate Council meeting. Please complete the Graduate Course Coordinator Form if the proposed changes will affect other units. Note: Colleges, Schools or Institutes are responsible for submitting new or modified catalog descriptions (35 words or less, using catalog format) to Creative Services by deadlines outlined in the yearly Catalog production calendar. Please indicate: New____X___ Modify_______ Delete_______ Department/Unit:____GGS_____________ Course Subject/Number:_______EOS 787 (GGS* 787)_________ Submitted by:_____________Ruixin Yang_________ Ext:___33615________ Email:_ryang@gmu.edu___ Course Title:___ Scientific Data Mining for Geoinformatics ______________________ Effective Term (New/Modified Courses only): __S2009_____ Final Term (deleted courses only):____________ Credit Hours: (Fixed) _3__ (Var.) ______ to ______ Grade Type (check one): ___X__ Regular graduate (A, B, C, etc.) _____ Satisfactory/No Credit only _____ Special graduate (A, B, C, etc. + IP) Repeat Status*(check one): __X_ NR-Not repeatable ____ RD-Repeatable within degree ____ RT-Repeatable within term *Note: Used only for special topics, independent study, or internships courses Total Number of Hours Allowed: _______ Schedule Type Code(s): 1._LEC LEC=Lecture SEM=Seminar STU=Studio INT=Internship IND=Independent Study 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: _____________ College/School Committee Approval Signature:__________________________________ Date:_____________ Graduate Council Approval Date:____________ Provost Office Signature:_________________________________
  2. 2. George Mason University Graduate Course Coordination Form Approval from other units: Please list those units outside of your own who may be affected by this new, modified, or deleted course. Each of these units must approve this change prior to its being submitted to the Graduate Council for approval. Unit: Head of Unit’s Signature: Date: Unit: Head of Unit’s Signature: Date: Unit: Head of Unit’s Signature: Date: Unit: Head of Unit’s Signature: Date: Unit: Head of Units Signature: Date: Graduate Council approval: ______________________________________________ Date: ____________ Graduate Council representative: __________________________________________ Date: ____________ Provost Office representative: ____________________________________________ Date: ____________
  3. 3. EOS/GGS 787, Scientific Data Mining for Geoinformatics Instructor: Ruixin Yang RSCHI 226, Tel: 993-3615, E-mail: 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: References Notes Prerequisite: competency in programming at the level of CSI601-607 or permission of instructor. Tentative Course Content: Week 1:Introduction to Data Mining Week 2:Mining Association Rules Week 3:Classification Week 4:Clustering Basic Week 5:Density-Based Clustering Week 6:Introduction to Spatial and Spatio-Temporal Data Week 7:Data Formats in Geosciences Week 8:Data Information Systems in Geosciences Week 9:Content-Based Geoscience Data Search Week 10:Geoscience Data Mining Applications I: Mining Climate Indices Week 11:Mining Spatial and Temporal Data Week 12:Geoscience Data Mining Applications II: Mining for Severe Weather Events Week 13:Geoscience Data Mining Applications III: Mining for Rapid Intensification of Tropical Cyclones Week 14:Student Project Presentation. Grading: Assignments: Assignments: 30%; Mid-term: 30%; Project: 40%
  4. 4. 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 object-oriented 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.
  5. 5. 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.