Data Mining

Course No.: SMJ2223311    Credit(s): 2

Course Description

Data Mining is a professional elective course in statistics. This course describes the main functions, mining algorithms and applications of data mining, and analyzes the commonly used data mining models through analyzing the actual data.

Course Learning Outcomes

Through the teaching of the Data Mining course, students can understand the basic concepts and methods of data mining, and learn and master the data mining methods in Modeler. Students can use Modeler software tools to carry out data mining and analysis.

Relationship to Other Courses

The prerequisites for this course are Statistics, Probability Theory and Mathematical Statistics, Statistical analysis of SPSS.

Textbook and Reading Lists

Textbook:

Wei Xue, Huasong Chen, Modeler Based Data Mining (1st edition). Renmin University of China Press, 2012.

Suggested reading lists:

Micheline Kamber, Jiawei Han, Data Mining: Concept and Technology (1st edition). Trans. Fan Ming, Xiaofeng Meng, Machinery Industry Press, 2001

Richard J. Roiger, Data Mining Tutorials. Trans. Jinglong Wen, Tsinghua University Press, 2003.

Jingming Cheng, Data Warehouse and Data Mining Technology. Electronic Industry Press, 2002.

Xun Liang, Data Mining Technology and Application, Peking University Press, 2006.

Paolo Giudici, Practical Data Mining. Trans. Fang Yuan, Electronic Industry Press, 2004.

Xiaodong Kang, Data Mining Technology Based on Data Warehouse. Machinery Industry Press, 2004.

Course Assessment

Course Schedule