Course name: Date mining
Course No.: 1080023 Credit: 2
Course Description
Date mining is a burgeoning interdiscipline. It is the rapidly rising computer technology in the field of information technology. Data mining techniques is mainly for application. Data mining plays a positive role in many important areas. Therefore this course is one of the important courses of computer science and related major. This course introduces the basic concepts of data mining, the principle, methods and techniques, including: data preprocessing, classification, prediction, association mining, clustering analysis and so on.
Course teaching objectives
The student learning outcomes are what student would be able to know and to do on the completion of this course. In details are:
1. Make the students understand the basic process of information mining (data mining);
2. Master the basic theory and technology of information mining, and be familiar with the information mining results show;
3. Master the basic methods of information mining, apply expertly information mining technology for effective analysis of real data;
4. Be able to get valuable information from a large number of statistical data combining with related statistical software.
Textbooks and References
Data Mining:Concepts and Techniques, Second Edition. Elsevier Inc. Jiawei Han & Micheline Kamber. (Mechanical Industry Press) , 2006.
References:
1.Han JiaWei, Micheline Kamber. Data Ming: Concept and technology. Mechanical Industry Press,2001.
2.Richard J. Roiger, Translated by Wong JingNong. Data Mining Course.TsinghuaUniversity Press, 2003.
3.Chen JingMin. Data Warehouse and Data Mining Technology. Electronic Industrial Press, 2002.
4.Kang XiaoDong. Data Ming Technology based on Data Warehouse. Mechanical Industry Press,2004.
5.Paolo Giudici. Translated by Yuan Fang. Practical Data Ming. lectronic Industry Press, 2004.
Course Assessment
Form | Weight (%) |
Attendance | 30% |
Final exam | 70% |
Course schedule
Week | Teaching content | Chapter |
1-2 | Prodromes | 1 |
3-5 | The OLAP technology of data warehouse and data mining | 2 |
6-8 | Data preprocessing | 3 |
9-10 | Concept description:characterized and compared | 4 |
11-13 | Mining association rules in large databases | 5 |
14-16 | Classification and prediction | 6 |
17-18 | Cluster analysis | 7 |
Total period:36
Chapter | Content | Period |
1 | Prodromes | 4 |
2 | The OLAP technology of data warehouse and data mining | 6 |
3 | Data preprocessing | 6 |
4 | Concept description:characterized and compared | 4 |
5 | Mining association rules in large databases | 6 |
6 | Classification and prediction | 6 |
7 | Cluster analysis | 4 |
Total | | 36 |