Undergraduate Program

Introduction to Data Mining

Course: CSC  297
Term: Fall 2012-2013

CSC 297/577 Intro to Data mining (Fall 2012)


Course description

Fundamental concepts and techniques of data mining, including data attributes, data visualization, data pre-processing, mining frequent patterns, association and correlation, classification methods, and cluster analysis. Advanced topics include outlier detection, stream mining, and social media data mining. CSC 577, a graduate-level course, requires additional readings and a course project. 

Prerequisites: MTH161, CSC171, CSC 172. Some knowledge of machine learning will be helpful.

Course schedule (tentative, chapters refer to the textbook)

- Overview and Introduction   (notes, Chap. 1)
- Getting to Know Your Data   (Chap. 2)
- Data Preprocessing   (Chap. 3)
- Review of Linear Algebra, Statistics   (notes)
- Pattern Recognition Concepts   (notes)
- Mining Frequent Patterns   (Chap. 6)
- Association and Correlation   (Chap. 6)
- Advanced Pattern Mining   (Chap. 7)
- Classification   (Chap. 8/9*)
- Cluster Analysis   (Chap. 10/11*)
- Advanced Topics: UR BIG DATA Forum October 4-6
- Advanced Topics: Social Media Mining  (Guest Lecture: Dr. Adam Sadilek) Nov. 6
- Advanced Topics: Bioinformatics   (Guest Lecture: Prof. Hulin Wu)
 - Advanced Topics: Multimedia Mining   (Special Lecture: Jiebo Luo)
- Advanced Topics: Influence Mining    (Guest Lecture: Dr. Saurabh Kataria)  Nov 8
* Advanced Topics: Heterogeneous Networks (notes)
* Advanced Topics: Outlier Detection   (Chap. 12)
- Trends and Research Frontiers   (Chap. 13, notes)
 
* Time Permitting

* Midterm Exam:  October 30 (October 25 review)

* Course Project presentation: 10 min. pp,  December 4, 6, 11, 13

 
 

Instructor and grading

Instructor: Prof. Jiebo Luo, CSB Rm 611, x65784
Lectures: TR 09:40-10:55, CSB 601
Office hours: after classes (15:15-16:00) or by appointment (use email).
TA: Iftekhar Naim,  office hours: Wedn. 2-3pm, CSB 728

Grading (total 100%)

  • homework assignments 35% (including a small project 10%)
  • midterm 30%
  • final project & presentation (for CSC 577) 30% (presentation counts 10%)
  • final survey & presentation (for CSC 297) 30% (presentation counts 10%)
  • class participation/effort 5%

Expectation for the final project -  something "new"

  • an existing algorithm applied to new data or new problems
  • a new algorithm (or a modified version of an existing algorithm) applied to the same data
  • new findings from a comparative study of using different algorithms for the same problem

* note: both the small project and the final project require programming


Textbooks and other resources

Required textbook

course information

Data Mining: Concepts and Techniques, 3/E
Jiawei Han, Micheline, Kamber, and Jian Pei

  • Publisher: Morgan Kaufmann, 2011
  • ISBN-10: 0123814790
  • ISBN-13: 978-0123814791

Recommended reference book

course information          

Social Media Modeling and Computing 
Steven C.H. HoiJiebo LuoSusanne BollDong XuRong JinIrwin King

  • Publisher: Springer, 2011
  • ISBN-10: 0857294350
  • ISBN-13: 978-0857294357

Major conferences

KDD 2012
WSDM 2013
ACM MM 2012
ICDM 2012

Sample datasets

Sample code

Three Chapters on Advanced Data Mining in the 2nd Ed. of the book