Undergraduate Program

Intro. to Data Mining

Course: CSC  297
Term: Fall 2013-2014

CSC 297/577 Intro to Data mining (Fall 2013)


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 (time permitting) 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 early October 
- Advanced Topics: Social Media Mining for Business and Economy (Guest Lecture: Prof. Huaxia Rui of Simon School) Nov. 4
- Advanced Topics: Bioinformatics   (Guest Lecture: Prof. Hulin Wu of URMC)
- Advanced Topics: Multimedia Mining   (Special Lecture: Prof. Jiebo Luo)
- Advanced Topics: Influence Mining    (Guest Lecture: Dr. Saurabh Kataria)  Nov. 6
- Trends and Research Frontiers   (Chap. 13, notes)

 

* Time Permitting

* Midterm Exam:  October 30 (October 28 review)

* Course Project presentation: 10 min. pp,  December 4, 5*, 9, 11

 
 

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: Quanzeng You,  office hours: Wedn. 2-3pm, CSB VISTA Lab (6th floor)

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

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

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

Recommended reference book

          

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 2014
WSDM 2014
ACM MM 2013
ICDM 2014
ICWSM 2014

Sample datasets

Sample code

 

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