Download introduction to data mining

‫‪Data Warehousing‬‬
‫‪and‬‬
‫‪Data Mining‬‬
‫‪IS-427‬‬
‫مستودعات البيانات و التنقيب عنها‬
‫نال ‪427‬‬
‫استاذ المادة‪ :‬ا‪ .‬والء ناجي‬
‫قسم نظم المعلومات‬
‫كلية علوم الحاسب والمعلومات‬
‫جامعة االميرة نورة بنت عبد الرحمن‬
‫‪Course website:‬‬
‫‪http://www.acadox.com/class/5367‬‬
‫ايميل ‪[email protected] :‬‬
The principal book(s) requested:
 "DATA WAREHOUSING FUNDAMENTALS:
A COMPREHENSIVE GUIDE FOR IT PROFESSIONALS", by Paulraj Ponniah.
 MODERN DATA WAREHOUSING, MINING, AND VISUALIZATION: CORE
CONCEPTS", by George M. Marakas.
 "INTRODUCTION TO DATA MINING", by Pang-Ning Tan, Michael Steinbach,
and Vipin Kumar.
 "DATA MINING: CONCEPTS AND TECHNIQUES", The Morgan Kaufmann
Series in Data Management Systems, by Jiawei Han, and Micheline Kamber.
 "DECISION SUPPORT SYSTEMS AND MEGAPUTER", by George M. Marak.
 "MANAGERIAL ISSUES OF ENTERPRISE RESOURCE PLANNING
SYSTEMS", by David L. Olson and David Olson.
 "DATA AND TEXT MINING: A BUSINESS APPLICATIONS APPROACH", by
Thomas W. Miller.
Topics to be discussed (theoretical content):
 Introduction to the course content, textbook(s), references
and course plan.
 Definition of knowledge discovery and data mining.
 Fundamentals of developing and using a data warehouse,
developing requirements, and designing models.
 Creating a dimensional model, generating population and
maintenance plans for a warehouse.
 Manipulating the data in the warehouse for update,
maintenance and data extraction.
 The use knowledge discovery in data warehouses.
 Data mining algorithms and methods including association
analysis, classification, cluster analysis.
 New emerging applications and trends in data mining.
Brief description of basic learning outcomes:
Students who successfully complete this course will
be able to:
Recognize the fundamentals of data warehousing.
Manipulate the data warehousing.
Recognize the basics of data mining.
Use the knowledge discovery in data warehousing.
Conduct different methods and algorithms of data mining.
Discover knowledge in different applications.
Schedule of Assessment Tasks according to which the students are evaluated
during the Semester
index The nature of the evaluation function
Due week
Assessment
weight (%)
1
First exam
Week 7
20%
2
Second exam
Week 12
20%
3
homework
4
Project+ Presentation
5
Final exam(Theoretical)
Total:
5%
During the
semester
After Week 15
100%
15%
40%