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.
Course schedule (tentative, chapters refer to the textbook)
Data Mining: Concepts and Techniques, 3/E
- 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) |
* 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
- 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
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
- 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