Dalhousie University    [  http://www.cs.dal.ca/~vlado/hinf6210  ]
Fall 2009 (Sep10-Dec7)
Faculty of Computer Science
Dalhousie University

HINF 6210 - Data Mining for Health Informatics and
ECMM 6014 - Data mining for Electronic Commerce

[ Shortcuts: Calendar | Project ]
Time: Tuesdays and Thursdays, 8:35-9:55
Location: McCain Arts and Social Sciences (FASS), room 2021
Instructor: Vlado Keselj, office: CS bldg 213, phone: (494)-2893, e-mail: vlado and add @cs.dal.ca
Office hours: "Open-door" policy, unless in a meeting or on a phone call. If you want to be sure that I am available, please make appointment by e-mail.
TA: Pif Edwards, email: medwards@dal.ca
E-mail list: dm-course@ lists.dnlp.ca

Course Descriptions

HINF 6210.03 : Databases and Data Mining for Health Informatics.
Health organizations collect massive amount of data to support clinical decision-making, outcome measurement, policy setting, administration, and research. This course provides a conceptual understanding of various data mining algorithms and introduces healthcare-related data mining strategies to facilitate the mining of real-life healthcare data to provide data-driven healthcare decision-support services. (link to calendar description)
ECMM 6014 : 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% Assignments
10% Project Presentation and Class Participation
30% Project Report (Project Guidelines)
30% Final test
Dalhousie Academic Integrity Policy

Course Calendar

Resources

References

Required Textbook:
  1. [HK] Data Mining - Concepts and Techniques by Jiawei Han and Micheline Kamber, Second Edition, Morgan Kaufmann, 2006, ISBN 1-55860-901-6. http://www-sal.cs.uiuc.edu/~hanj/bk2/.
Recommended Textbooks:
  1. Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten and Eibe Frank, Second Edition, Morgan Kaufmann, 2005, ISBN 0-12-088407-0.
  2. Data Mining Using SAS Enterprise Miner by Randall Matignon, Second Edition, Wiley, 2007, ISBN ISBN 978-0-470-14901-0.
  3. Pattern Recognition and Machine Learning by Chrisopher M. Bishop, Springer, 2006, ISBN 0-38-731073-8.
  4. Data Mining - Introductory and Advanced Topics by Margaret H. Dunham, Prentice Hall, 2003, ISBN 0-13-088892-3.
  5. 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.
© 2004-2009 Vlado Keselj, last update: 13-Oct-2009