第一部份:99學年度第一學期授課大綱

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Syllabus
Instructor
Department & Class
Deng-yiv Chiu
Department
of
Information
Management (Master Degree)
Course Name
Credit
Elective
3
Data mining
Data mining is the process of discovering new patterns from large data sets.
This course is aimed at introducing the concepts of data mining and its application.
Course
Description
Through this course, the student will understand how to exact useful information from
massive data.
Also, the critical data mining techniques will be introduced, such as
classification, cluster, association analysis etc.
This course aims to raise the student’s interest in data mining. Therefore, the
course materials include fundamental, advanced and diverse complemented course
materials. Also, various data mining application software will be introduced.
Learning Goals
Course Materials
References
Therefore, the student will understand the theories of data mining.
The student will learn how to utilize the application software of data mining:
Cluster 3.0, Answer Tree, CAFÉ, Libsvm, Gene Hunter, XLMiner and MATLAB etc.
There are many research and literature related to data mining. Therefore, the student
needs to study a lot of paper to understand the emerging data mining techniques. It
will help the student to utilize the data mining techniques in his/her own research.
1.Pang-Ning Tan, Michael Steinbach, Vipin Kumar (2006), Introduction to Data
Mining: International Edition, PEARSON, ISBN: 0-321-42052-7
2.Data Mining; Author: Ian H. Written, Eibe Frank Publisher:Morgan Kaufmann
1.
2.
Expert Systems with Applications
Information Processing and Management
Week 1: Introduction
Week 2: Data
Week 3: Data
Week 4: Exploring Data
Week 5: Exploring Data
Teaching
Week 6: Classification: Basic Concepts, Decision Trees, and Model Evaluation
Schedule &
Week 7: Classification: Basic Concepts, Decision Trees, and Model Evaluation
Contents
Week 8: Classification: Alternative Techniques
Week 9: Classification: Alternative Techniques
Week 10: (MIDTERM)
Week 11: Association Analysis: Basic Concepts and Algorithms
Week 12: Association Analysis: Advanced Concepts and Algorithms
Week 13: Association Analysis: Advanced Concepts and Algorithms
Week 14: Cluster Analysis: Basic Concepts and Algorithms
Week 15: Cluster Analysis: Advanced Concepts and Algorithms and Algorithms
Week 16: Cluster Analysis: Advanced Concepts and Algorithms and Algorithms
Week 17:Anomaly Detection
Week 18:FINAL EXAM