CS570 Data Mining, Fall 2017
Lecture: MW 10-11:15am MSC W301
Instructor: Li Xiong (lxiong @ emory.edu)
OverviewThis course offers an introduction to data mining concepts and techniques. It complements CS534 Machine Learning. The focus will be on key data mining algorithms and selected data mining applications. Topics include: data preprocessing, frequent pattern mining and association analysis, cluster analysis, outlier detection, ranking and skyline, spatiotemporal data mining, and privacy preserving data mining.
TextbookData Mining: Concepts and Techniques, Third Edition. Jiawei Han, Micheline Kamber, Jian Pei
The course will be supplemented with materials from other reference books and recent research papers.
PrerequisitesCS534 Machine Learning or knowledge about machine learning, database systems, and statistics will be helpful. Familiarity with a programming language, such as Java or C++, is required for programming assignments and/or final project.
AssignmentsThere will be reading and programming assignments, spaced out over the first 2/3 of the semester (the last 1/3 of the semester is reserved for the final course project). The typical time frame is one week for reading assignments and two weeks for programming assignments. For programming assignments, you will be implementing classicial data mining or machine learning algorithms. For reading assignments, you will read research papers and submit a written review. You are expected to present one paper in class.
You have 1 late assignment allowance, which can be used to turn in a single late assignment within 3 days of the due date without penalty. Otherwise, late assignment will be accepted within 3 days of the due date and penalized 10% per day. No extensions will be given. Please note on the assignment if you wish to use the late assignment allowance.
ExamsThere will be one open-book midterm exam and no final exam.
ProjectThere will be a substantial course project. Different project ideas and options will be discussed and posted. Project deliverables include project proposal, in-class project presentation, project report, source code and executable package if applicable.