CS 535 Introduction to Data Mining Fall 2005
This data mining course introduces the concepts, algorithms, techniques, and applications of data mining. Topics include background of data mining, data preprocessing, classification, clustering, mining association rules, and mining complex types of data from application domains (e.g., relational data, Web data, steam data, and biomedical data). This course is designed for CS graduate students, while senior CS undergraduate students interested in the field are welcome to talk to the instructor to determine whether they are qualified for taking this course.
|Class Schedule: T TH 10:05 AM - 11:30 AM||Classroom: FA 241|
||TA: Hao Li|
|Telephone: (607) 777-6250||Telephone:|
|Email: lyu AT cs DOT binghamton DOT edu||Email: hli1 AT binghamton DOT edu|
|Office Location: N 21, Engineering Building||Office Location: LSG 562|
|Office Hours: T TH 12:30PM - 2:00PM or by appointment||Office Hours:|
This course does not require any textbook, however, the following two textbooks are highly recommended.
Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison-Wesley, April 2005.
Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2001.
Reading and Presentation: (paper list)
There will be periodical reading assignments before lectures through the semester. In addition, students are required to read recent research papers in selected topics and present papers in groups of two (or individually). The presenters will also be responsible for conducting group discussions and answering questions.
There will be 3 written assignments during the semester.
There will be two individual projects involving implementation and evaluation of data mining algorithms for different data mining tasks, like preprocessing, classification, and clustering.
There will be two exams during the semester.
Final grades will be based on quiz (10%), homework (3 assignments, 15%), project (2 assignments, 20%), paper presentation (15%), and exam (2 exams, 40%).
Student Academic Honesty Code
Any form of collaboration among students is prohibited for all quizzes, homework assignments, and exams, except otherwise noted. For course projects, each student must individually finish all implementation and evaluation tasks and hand in their own project reports. It is acceptable, however, for students to collaborate and help each other in figuring out solutions to the projects. We will assume that, as participants in a graduate course, you will take the responsibility to make sure you personally understand the solutions to any part of the projects arising from such collaboration. If any kind of academic dishonesty is found, an "F" will be issued as the final grade of this course. Additional penalty is subjected to the decision of the faculty in the department.
Each assignment is due at the beginning of class on the due date. Any assignment received within the next 24 hours will be penalized by 20% of the full credit; any assignment received within the time between 24 hours and 48 hours pass the deadline is penalized by 50% of the full credit; No assignment will be accepted after 48 hours pass the deadline.
Last updated on 10/11/2005