Course No.: SMJ2223311 Credit(s): 2
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
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.
Task in home
Test and Questions in class
Introduction to Data Mining
Data Reading and Integration of Modeler
Data Understanding and Data Preparation
The Basic Analysis of Modeler
Data Streamlining of Modeler
Classification and Prediction