CS 635 Advanced Data Mining Seminar

Fall 2011


This course is a graduate seminar focusing on the state-of-the-art research issues in the area of data mining.  Students taking this course will learn advanced techniques and algorithms for data mining as well as emerging data mining applications in interdisciplinary domains such as autonomic computing, green computing, and social computing.

This course is designed for CS graduate students, while graduate students from other departments who are interested in data mining research can take this course with instructor approval.

Class Schedule: Friday 1:10 PM - 4:10 PM

Instructor: Dr. Lei Yu  

Telephone:  (607) 777-6250

Email: lyu AT cs DOT binghamton DOT edu  

Office Location: G16, Engineering Building

Office Hours: Wednesday 1:00PM - 3:00PM, other times by appointment only



This course does not require any textbook.

Paper Reading and Presentation:

This is a graduate seminar course that will focus on recent literature on advanced techniques and algorithms for data mining and their applications to real-world problems from a range of different domains. Each student is required to study recent research papers on selected topics, and present three papers in class. The presenters will also be responsible for conducting group discussions and answering questions.


Each student is required to carry out a research project under the instructor's guidance. The types of projects include: (i) implementation of an existing advanced data mining algorithms for a particular data mining problem; (ii) an in-depth literature survey on a specific topic of interest; (iii) development of a new data mining algorithm which improves the existing ones; or (iv) application of data mining techniques to a real-world problem. A number of candidate projects will be provided by the instructor and introduced in class at the beginning of the semester. Each student is required to give four oral presentations on the selected project: a project proposal after the introductory classes, two intermediate project reports in the middle of the semester, and a final report at the end of the semester. In addition, each student is required to submit a written project report at the end of the semester.


Final grades will be based on class participation 10%, paper presentation 30% (10% for each paper presentation), and project 60% (10% for each oral presentation and 20% for written project report).