1. CSCE 4143
Section 001: Data Mining
Spring 2019

2. Overview Class hour 9:30 – 10:45am, Tuesday & Thur, JBHT 239
Office hour 11:00 – 12:00pm,Tuesday & Thur, JBHT 516
Instructor - Dr. Xintao Wu -
Office – JBHT 516
Webpage
Textbook
Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, ISBN:

3. Topic Description Introduction to data mining Know your data
Data preprocessing
Frequent pattern mining, association and correlation
Classification
Cluster analysis
Graph analysis
Outlier Detection
Advanced topics
Deep learning
Big data analysis including MapReduce, Spark
Social aware data mining 3

4. Course Prerequisite Data Structure and algorithm
Familiarity with programming with Java or C++ is assumed
Matlab/R/Python/Scala is preferred.
Probability and statistics basic concept
Knowledge of linear algebra is a big plus 3

5. Grading Composition Homework and quiz 10% Project 30% Midterm 20%
Final % 3

6. Homework and Project Reports
Late policy:
No acceptable.
Hard copy is preferred
Electronic submission (word or pdf) accepted 3

7. Project Each group consists 2-3 students
Develop, implement, and apply data mining techniques to solve the data analysis problems
Practice Project
10 points
focus on Adult dataset with specific tasks
Data Analysis Project
20 points
focus on a real-world data analysis problem
More information 3

8. Midterm & Final Open books/notes/internet Cumulative No makeup
No discussion
No help from any entity, e.g., by posting/uploading your questions on Web
Cumulative
No makeup
Class attendance is not required
Bonus is expected 3

9. Textbook & Recommended Reference Books
5/23/2019
Textbook & Recommended Reference Books
Textbook
Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011
Recommended reference books
C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2007.
S. Chakrabarti, Mining the Web: Statistical Analysis of Hypertext and Semi-Structured Data, Morgan Kaufmann, 2002
T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction,2nd ed., Springer-Verlag, 2009.
B. Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer,
D. Easley and J. Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge Univ. Press, 2010.
M. Newman, Networks: An Introduction, Oxford Univ. Press, 2010. 9

10. Reference Papers Course research papers: Check Reading_List
5/23/2019
Reference Papers
Course research papers: Check Reading_List
Major conference proceedings that will be used
DM conferences: ACM SIGKDD (KDD), ICDM (IEEE, Int. Conf. Data Mining), SDM (SIAM Data Mining), PKDD (Principles KDD)/ECML, PAKDD (Pacific-Asia)
DB conferences: ACM SIGMOD, VLDB, ICDE
ML conferences: NIPS, ICML
IR conferences: SIGIR, CIKM
Web conferences: WWW, WSDM
Other related conferences and journals
IEEE TKDE, ACM TKDD, DMKD, ML,
Use course Web page, DBLP, Google Scholar, Citeseer
CS591Han: Advanced Seminar on Data Mining 10

11. Research Frontiers in Data Mining
5/23/2019
Research Frontiers in Data Mining
Mining social and information networks
Mining spatiotemporal data, moving object data & cyber-physical systems
Mining multimedia, social media, text and Web
Data software engineering and computer system data
Multidimensional online analytical analysis
Pattern mining, pattern usage, and pattern understanding
Biological data mining
Stream data mining 11