The knowledge discovery and data mining discipline is primarily based on databases, statistical methods, neural networks, and machine learning. This course presents the selected topics from these areas that are relevant to data mining algorithms, including their applications and selected recent developments.
PREREQ: CIS 675, ELE 606, CSE 607

Class: Mon 5:15 p.m. – 8:15 p.m.

Class Location
CST 4-201

Course Instructor
Dr. Amrit L. Goel
Office: CST 4-193
Phone: 315-443-4350
Office Hours :

Course Teaching Assistant
Rajika Tandon
Office Hours (tentative) for rapid miner/labs: Tuesdays 4:00 - 6:00 pm, (near) CST 3-116, or by appointment

Course Scope
The course will discuss the following topics:
- Knowledge discovery process and data mining; CRISP model
- Data warehouses and OLAP including data cubes
- Statistical inference and regression
- Radial basis function models for function approximation and classification using a new algebraic approach
- Classification and Clustering: Quinlan’s algorithm for classification trees, k-means.
- Neural networks and the back propagation algorithm
- Rule generation: basic algorithm
- Applications (case studies) from software engineering, astronomy, medical diagnosis, telecommunications, etc.
- Additional selected topics such as support vector machines (SVM) with emphasis on applications

Course Materials
• J. Han and M. Kamber. Data Mining Concepts and Techniques, Morgan Kaufmann, Second Edition, 2006 (We cover about 30% of this book); ISBN 1-55860-9016.

• Miyoung Shin and Amrit L. Goel, Lecture notes on analytical data mining (Draft).

• Selected papers, notes, and other material based on students’ research and papers.

• Amrit L. Goel and Miyoung Shin, Radial Basis Functions: An Algebraic Approach ( with Data Mining Applications), Tutorial Notes , 15th European Conference on Machine Learning and 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 2004

Class Preparation
You are expected to read the appropriate sections of the text prior to the class in which the material will be discussed. Any assignments due that day are due at the beginning of the class.

You are expected to attend every class. You will be responsible for any announcements and materials covered and any work that is due.

Class Documents