Dalhousie University    [  http://www.cs.dal.ca/~vlado/2003/csci6405  ]
Winter 2003 (Jan-Apr)
Faculty of Computer Science
Dalhousie University

CSCI 6405 - Data Mining and Data Warehousing
ECMM 6014 - Databases, Data Warehouses and Data Mining for Electronic Commerce

[ Shortcuts: Calendar ]
Time: Tuesdays and Thursdays, 10:05-11:25
Location: McCain Arts and Social Sciences, room 2017
Instructor: Vlado Keselj, office: CS bldg 213, phone: (494)-2893, e-mail: vlado @ cs. dal. ca
TA: ChunMei (May) Gao, email: cgao @ cs. dal. ca
Newsgroup: dal.csci.csci6405

Course Description

CSCI 6405.03 : Data Mining and Data Warehousing
This class gives a basic exposition of the goals and methods of data mining and data warehouses, including concepts, principles, architectures, algorithms, implementations, and applications. The main topics include an overview of databases, data warehouses and data mining technology, data warehousing and on line analytical process (OLAP), concept mining, association mining, classification and predication, and clustering. Software tools for data mining and data warehousing and their design will also be introduced. (link to calendar description)
ECMM 6014.03 : Databases, Data Warehouses and Data Mining for Electronic Commerce.
Data warehousing and data mining are two emerging technologies which will have a profound effect on the role information plays in organizations. A data warehouse is a repository of data taken from multiple sources that supports querying and analysis tools. Data mining, the process of knowledge discovery from data in a data warehouse, is typically used for strategic planning and has great economic potential for organizations. This class covers key issues in data warehouse architecture, design of data warehouse schemas, design of metadata repositories, the creation, development and maintenance of warehouses, as well as tools and techniques for querying, analyzing and mining the warehouse data. Data mining techniques such as statistical and non-statistical supervised and unsupervised learning methods will be applied to problems drawn from the medical and business world. (link to calendar description)

Evaluation

30% 3 Assignments
10% Project Presentation and Class Participation
30% Project Report (Project Guide)
30% Final exam
Plagiarism Policy

Course Calendar
Review Questions for the final exam

References

Required Textbook:
  1. [HK] Data Mining - Concepts and Techniques by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2001, ISBN 1-55860-489-8, http://www-faculty.cs.uiuc.edu/~hanj/DM_Book.html.
Recommended Textbooks:
  1. Data Mining - Introductory and Advanced Topics by Margaret H. Dunham, Prentice Hall, 2003, ISBN 0-13-088892-3.
  2. Principles of Data Mining by D. Hand, H. Mannila, and P. Smyth, MIT Press, 2001.
Related Books:
  1. Data Mining - A tutorial-based primer by Richard J. Roiger and Michael W. Geatz, Addison Wesley, 2003, ISBN 0-201-74128-8.
  2. Data Mining - Building Competitive Advantage by Robert Groth, Prentice Hall, 2000, ISBN 0-13-086271-1.
  3. Modern Data Warehousing, Mining and Visualization by George M. Marakas, Prentice Hall, 2002, ISBN 0-13-101459-5.
© 2003 Vlado Keselj, last update: April 3, 2003.