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.
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) |
* 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
- Publisher: Morgan Kaufmann, 2011
- ISBN-10: 0123814790
- ISBN-13: 978-0123814791
Recommended reference book
Social Media Modeling and Computing
Steven C.H. Hoi, Jiebo Luo, Susanne Boll, Dong Xu, Rong Jin, Irwin 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
- Source codes for Frequent Pattern Mining, Clustering, Time Series and Web Mining allgorithms implemented by Chinese Univ. of Hong Kong: http://appsrv.cse.cuhk.edu.hk/~kdd/program.html
- FIMI workshops: Datasets and source codes for frequent itemset mining implementations: http://fimi.cs.helsinki.fi/
- Frequent itemset mining algorithm implementations by Bart Goethal: http://www.adrem.ua.ac.be/~goethals/software/
- Repository of implementations of UIUC data mining research package: IlliMine http://illimine.cs.uiuc.edu/edu/
- Weka: Weka 3 - Data Mining with Open Source Machine Learning Software in Java: http://www.cs.waikato.ac.nz/ml/weka/
- Graph mining algorithm implemtations: gSpan and CloseGraph java implementation
- Chapter 8. Mining Stream, Time-Series, and Sequence Data
- Chapter 9. Graph Mining, Social Network Analysis, and Multirelational Data Mining
- Chapter 10. Mining Object, Spatial, Multimedia, Text, and Web Data