CS378 Data Mining, Spring 2018

Lecture: MW 10-11:15am MSC W201

Web: http://www.mathcs.emory.edu/~lxiong/cs378
Mailing List: cs378000-list@mathcs.emory.edu

Instructor: Li Xiong (lxiong [at] emory [at] edu)
Office hours: M 2-3pm W 11:15-12:15pm or by appt, MSC E412
TA: Derek Onken (donken [at] emory [dot] edu)
Office hours: Tu 11-12am F 10-11am or by appt, MSC N411 (Symbiosys lab / Attic storage)


This course offers an introduction to data mining and machine learning concepts and techniques. The focus will be on use and implementation of key data mining and machine learning algorithms. Topics include: data preprocessing, association analysis, classification, cluster analysis, link analysis, recommender systems as well as emerging applications and trends in data mining.


Data Mining: Concepts and Techniques, 3rd Edition. Jiawei Han, Micheline Kamber, Jian Pei

Mining of Massive Datasets. J. Leskovec, A. Rajaraman, J. Ullman

The course will be supplemented with materials from other reference books.


CS323 or equivalent: familiarity with a programming language, such as Java or C++, and data structures. Some knowledge about database systems and statistics will be helpful.


There will be written 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 written assignments and two weeks for programming assignments. For programming assignments, you will be implementing classicial data mining or machine learning algorithms.

Late Policies

You have 2 late assignment allowances, each 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 email the TA and cc me if you wish to use the late assignment allowance.


There will be one midterm exam and a final exam.


There 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.


Component Weight

Final project15
Score Grade

93-100 A
90-92.9 A-
87-89.9 B+
83-869 B
80-82.9 B-
Enjoy yourself and good luck!