Fall 2012   CSE4334 / 5334   Data Mining

Course Description: This is an introductory course on data mining. Data Mining refers to the process of automatic discovery of patterns and knowledge from large data repositories, including databases, data warehouses, Web, document collections, and data streams. We will study the basic topics of data mining, including data preprocessing, data warehousing and OLAP, data cube, frequent pattern and association rule mining, correlation analysis, classification and prediction, and clustering, as well as advanced topics covering the techniques and applications of data mining in Web, text, big data, social networks, and computational journalism.

Student Learning Outcomes: A solid understanding of the basic concepts, prunciples, and techniques in data mining; an ability to analyze real-world applications, to model data mining problems, and to assess different solutions; an ability to design, implement, and evaluate data mining software.

Prerequisites:

• prerequisites for CSE4334: CSE 3330 Database Systems I   and   IE 3301 Engineering Probability (or MATH 3313 Introduction to Probability)   or   consent of instructor
• prerequisites for CSE5334: CSE 3330/5330  Database Systems I   or   CSE 4331/5331  Database Systems II   or   similar courses   or   consent of instructor

Textbook

• (Required) Jiawei Han, Micheline Kamber and Jian Pei . Data Mining: Concepts and Techniques, 3rd ed.  (3rd or 2nd editions preferred, 1st edition is also fine), Morgan Kaufmann Publishers, June 2011. ISBN 9780123814791
• (Reference) Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, Addison-Wesley, 2006. ISBN 0-321-32136-7.
• (Reference) Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. (This book is available online at http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html)
• (Reference) I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005.
• (Reference) T. M. Mitchell, Machine Learning, McGraw Hill, 1997.

Grades

• Midterm Exam 20%    (Monday, Oct. 15th, 4-5:20pm, NH 202)
• Final Exam   35%    (Wednesday, Dec. 12th, 2-4:30pm, NH 202)
• Homework (HW)  15%   (Must be done independently)

• Course Project 30%   (Independently or in group of 2)

The final letter grades will be based on the curve of students' performace. Undergraduate and graduate students are to be compared in 2 separate groups.

Attendance: Students are required to attend lectures.

Announcements: Stay tuned and make sure to check Blackboard frequently. Important announcements will be posted there.

Assignments and Deadlines

• All the assignments must be submitted through Blackboard. We will NOT take hardcopy or email submission, unless the school verifies that Blackboard was malfunctioning or unavailable.
• Everything is due by 11:59pm on the due date. The deadline is automatically managed by Blackboard. You can still turn in assignment after the deadline. However, you automatically lose 5 points per hour after the due time, till you get 0. (Each individual assignment is 100 points.) We cannot waive the penalty, unless there was a case of illness or other substantial impediment beyond your control, with proof in documents from the school.

Regrading: Regrading request must be made within 7 days after we post scores on Blackboard. TA will handle regrade requests. If student is not satisfied with the regarding results, you get 7 days to request again. The instructor will regrade, and the decision is final.

Drop Policy: Students may drop or swap (adding and dropping a class concurrently) classes through self-service in MyMav from the beginning of the registration period through the late registration period. After the late registration period, students must see their academic advisor to drop a class or withdraw. Undeclared students must see an advisor in the University Advising Center. Drops can continue through a point two-thirds of the way through the term or session. It is the student's responsibility to officially withdraw if they do not plan to attend after registering. Students will not be automatically dropped for non-attendance. Repayment of certain types of financial aid administered through the University may be required as the result of dropping classes or withdrawing. For more information, contact the Office of Financial Aid and Scholarships (http://wweb.uta.edu/ses/fao).