Description

Fundamental concepts of data mining and the techniques and issues associated with analyzing large data sets are covered in this course. Topics include: Data warehouse and OLTP technologies for data mining, classification of data mining techniques and models, data pre-processing, data mining primitives, query languages and system architecture, characterization and comparison, association rules, cluster analysis, multidimensional analysis and descriptive mining of complex data objects, data mining in distributed heterogeneous database systems, data mining applications and future research issues.

Prerequisites:

• CEN 306 + Lab : Algorithms

• CEN 401 + Lab : Database Management

• CEN 305 + Lab : Object-Oriented Programming

Expected Outcome(s):

• Understand the basic principles for data cleaning and data transformation and apply typical methods of data cleaning and transformation in the context of data mining.
• Understand the basic principles of data warehousing and data cubing and apply typical methods of data warehousing and data cube computation.
• Understand the basic principles for mining frequent patterns and apply typical frequent pattern mining methods for effective data mining.
• Understand the basic principles for classification and apply typical classification methods for effective data mining.
• Understand the basic principles for data clustering and apply typical clustering methods for effective data mining.

Suggested Books:

1. Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei
2. Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank and Mark A. Hall
3. Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar

Grading Policy:

Biweekly Quiz, One Midterm Exam, One Final Exam, Project

Letter Grade	Marks	Grade Point
A	90 - 100	4.00
A-	85 - 89	3.70
B+	80 - 84	3.30
B	75 - 79	3.00
B-	70 - 74	2.70
C+	65 - 69	2.30
C	60 - 64	2.00
C-	55 - 59	1.70
D+	50 - 54	1.30
D	45 - 49	1.00
F	00 - 44	0.00

---