CS 5730: Data Mining
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CS 5730: Data Mining (資訊探勘)

Dr. Fenn-Huei Simon Sheu (許奮輝)

Phone: (03) 574-2959, Office: 資電館 638 室

Email: sheu@cs.nthu.edu.tw

Fall 2005

 Objective:

      The objective of this course is to prepare the students for performing research in the area of data mining, one of actively evolving fields in industry and academia. The techniques and algorithms introduced are of practical utility to meet the extensive multidisciplinary demands of this fast developing field. In particular, the focus of the course is to enlighten the students on their inherent apprehension of the arts – the rationales behind the concepts and properties of the applied methods. Such in-depth realization will be easily powered by the empirical practices to develop creative problem-solving strategies to work on the next-generation computing environment.

Prerequisite: Calculus, Linear Algebra, Engineering Mathematics, Self-motivation.

Class Time & Room: M5M6F5, 資電館 132 室

Office Hours: T7T8, R7R8, or by appointment.

Lecture notes & Homework assignments:

  • Please visit the "Data Mining" course pages maintained by TAs for daily information.

Textbook:

  •  Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques," (ISBN: 1-55860-489-8), Mogan Kaufmann Publishers, 2001.

Reference:   

  • George M. Marakas, "Modern Data Warehousing, Mining, and Visualization: Core Concepts," (ISBN: 0-13-101459-5), Prentice Hall, 2003.

  • Richard J. Roiger and Michael W. Geatz, "Data Mining: A Tutorial-based Primer," (ISBN: 0-201-74128-8), Addison-Wesley, 2003.

  • Mehmed Kantardzic, "Data Mining: Concepts, Models, Methods, and Algorithms," (ISBN: 0-471-22852-4), Wiley Inter-science, 2003.

  • Lecture notes.

Topics:

 

Data Warehouse and OLAP

Data Preprocessing

 

Data Mining Primitives

Concept Description

 

Association Rules Mining

Classification and Prediction

 

Cluster Analysis

Mining Complex Types of Data

 

Applications and Trends in Data Mining

 

Grading:

  • Three Critical Reviews: 30%,

  • Presentation (peer reviews) with Term Paper (project): 40%, and

  • Final Exam: 30%..

Tentative Schedule:

 

Each topic every one or two weeks.

 

Presentations: start on the last two weeks