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Undergraduate Program

Data Mining

Course: CSC  240
Term: Fall 2017-2018

CSC 240/440 Data mining (Fall 2017)


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 240, an undergraduate-level course, will receive up to 5% bonus for meeting the same requirements as CSC 440. 

Prerequisites: MTH161, CSC171, CSC 172. Some knowledge of artifical intelligence (CSC 242) or probability theory (CSC 262) will be helpful. 

Course schedule (tentative, chapters refer to the 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
- 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*)
- Outlier Detection(Chap. 12)
- Advanced Topics: Social Multimedia Mining (notes)
- Advanced Topics: Biomedical Informatics  (Guest Lecture: Prof. Martin Zand of URMC)
- Advanced Topics: Network Mining  (Guest Lecture: Prof. Gourab Ghoshal)
- Advanced Topics: Influence Mining   (Guest Lecture: TBD)
- Trends and Research Frontiers  (Chap. 13, notes)

 

* Time Permitting

* Midterm Exam:  October 31 (October 26 review)

* Course Project presentation: 10 min. pp,  December 5, 7, 12, 14

 
 

Instructor and grading

Instructor: Prof. Jiebo Luo, Wegmans Hall Rm 3101, x65784
Lectures: TR 12:30-13:45, Goergen Hall 101
Office hours: after classes (15:00-16:00) or by appointment (use email).
TA: Tianlang Chen, Haofu Liao, Numair Sani, Yiming Pan, office hours: M/T/W/R, 2-3pm, Computer Science VIStA Lab (3504 Wegmans Hall)

Grading (total 100%)

  • homework assignments 35% (5% for each of the 5 assignments, plus a small project 10%)
  • midterm 30%
  • final project & presentation 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 (Python recommended)


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

Mining of Massive Datasets, 2/E 
Jure Leskovec, Anand Rajaraman, Jeffery David Ullman

  • Publisher: Cambridge University Press, 2011
  • ISBN-13: 978-1107077232

- Major conferences

KDD, CIKM, ICWSM

- Sample datasets

- Sample code

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