COMP 4332 / RMBI 4310
M/W 12pm--1:20pm, Room 3006 (Lift 17/18)
Tutorials: Thur. 6pm-6:50pm
Keywords: Large-scale data mining, Data analytics, Social Media and Networks, Social Recommendation.
Instructor: Professor Qiang Yang. Office Hours: TBD
Teaching Assistants: Yin Zhu, Kaixiang Mo. Office Hours: TBD
Link to Lecture Notes
Link to Course Newsgroup
Students will understand issues related to real-world data mining;
Students will master tools and skills for large-scale data mining projects;
Students will gain experience on recent topics in business intelligence and social media mining.
This is a project oriented course. It will expose students to practical issues of large-scale and real world data mining. Data mining is a process of extracting implicit, previously unknown, and potentially useful knowledge from data, and it is a critical task in many applications. This course will place emphasis on applications of data mining on areas such as business intelligence, which aims to uncover facts and patterns in large volumes of data for decision support. Application areas also include many other areas in science and engineering applications. This course builds on basic knowledge gained in the introductory data-mining course, and explores how to more effectively mine large volumes of real-world data and to tap into large quantities of data. It will introduce new algorithms that can more effectively find hidden and profitable data patterns and knowledge. Working on real world data sets, students will experience all steps of a data-mining project, beginning with problem definition and data selection, and continuing through data exploration, data transformation, sampling, portioning, modeling, and assessment.
Prerequisite: COMP 4331 or equivalent (stats and machine learning/pattern recognition coureses; please check w/ instructor)
Data Mining Algorithms for Business Intelligence: Credit rating, customer relationship management
Large-scale Data Mining Algorithms
Social Recommendation, Social Networks and Social Media
Assignments (10%)
Projects: Code, Demos, Reports and Term Papers (40%)
Presentations (10%)
Exams (40%)
Research and industrial papers from journals, magazines and conferences.
Introduction to Machine Learning, by Ethem Alpaydin. The MIT Press. Second Edition.
Business Intelligence by Carlo Vercellis. Wiley.
Community Detection and Mining in Social Media by Lei Tang and Huan Liu. Morgan & Claypool Publishers.
Updated on Jan 15, 2012