Catalog Course Description:
This course introduces the concepts and principles of knowledge discovery in databases (KDD), with a focus on the techniques of data mining and its function in business, governmental, medical and other information-intensive environments. 

Pre-requisites and Co-requisites:
This course is intended for the advanced MS, MSIS, CAS, or Ph.D. student who is specializing in information systems.  A background in programming is highly recommended. 
Specific prerequisite courses are:
INFO 605  Database Management I
INFO 629  Artificial Intelligence or INFO 612  Knowledge Base Systems

Curriculum Role:
This course is a domain course for PH.D. students. Ph.D. students in the IS program typically take it in the second year after they finish the 5 core courses.

Course Rationale:
This course is offered to provide students with advanced knowledge in data mining technique, algorithm and methods. Students learn data pre-processing, various data mining algorithms  including supervised learning, semi-supervising learning and unsupervised learning.

Course Outcomes:
Upon successful completion of this course, a student will be able to:
• Understand the issues of KDD, its history, uses, and motivation.
• Understand the importance of data pre-processing and techniques for accomplishing it
• Become familiar with a variety of data mining techniques and will perform them on practice databases, understanding which tools are appropriate for which data mining tasks
• Understand how to develop and evaluate a data mining enterprise in the context of the KDD life cycle
• Learn how to assist clients with evaluating the appropriateness and efficacy of a variety of data mining methods, as well as the output from the data mining enterprise

Course Content:
Principal topics and the approximate number of weeks devoted to each are:
• Introduction (1)
• Data warehouse and OLAP technology for data mining (1)
• Data pre-processing methods (1)
• Association rules (1)
• Classification and prediction (2)
• Cluster analysis and other unsupervised methods (1)
• Mining complex data (1)
• Data mining in bioinformatics (1)
• Data mining in various work environments (1)

Presentation:
Note: Presentation method may vary somewhat from section to section.
The principal method of presentation is by lecture, in-class presentation, and class discussion. 

Assessment:
Note: Assessment method may vary somewhat from section to section.
Evaluation is by examination and homework assignments or project in each of the major areas described above.