Download اسم الكورس: التصميم الفيزيائي والتطبيق I

Course Code: IM433
Course Title: Data Mining and Data Warehousing
Credit Hours
 4 Hours, divided into three hours of lecture and three hours for lab session
Course Description
This course covers the data mining concepts It will introduce various data
mining techniques. The course introduces also data warehouse concepts
Course Objective
Learning Outcomes
Upon completion of this course, students will have the ability to:
1. Understand Data Warehouse and OLAP technology for data mining: Data
preparation, data mining primitives, languages, and system architectures,
2. Make mining association with rules in large databases, do classification
and prediction (with various techniques: Cluster analysis, neural nets, and
genetic algorithms.),
3. Develop and understand data mining applications and trends of data
mining,
4. Deal with warehousing strategy, warehouse management and support
processes,
5. Have skills in data warehouse planning, data warehouse implementation,
data warehouse maintenance and evolution,
6. Use some warehouse software related to some warehouse applications,
and be acquainted with recent warehouse trends.
Course contents
 Data mining concepts: why data mining?, cycles of data mining, the
various cycles in practice, data mining methodology, measurement of
the effectiveness of data mining.
 various data mining techniques: the market based analysis, clustering,
link analysis, decision trees, artificial neural networks, genetic
algorithms, data mining and the corporate data warehouses, OLAPs, and
choosing the right tool for the job, putting data mining to work.
 Data warehouse concepts: Gradual changes in computing, dynamic
reports, data Marts, operational Data stores, and data warehouse costbenefit analysis.
 Some other concepts are described such as: Warehousing strategy,
warehouse management and support processes, data warehouse
planning, data warehouse implementation, data warehouse maintenance
and evolution, warehouse applications and warehouse software, and
recent warehouse trends. Student will be trained on some well-known
data mining software like: Matryx98, Cart, Megaputer PolyAnalyst,
KnowledgeAccess, Cognos Power Play.
Teaching Methods
 Lectures and laboratories
Assessment
 Final Examination. (50%)
 Tests, reports and Homeworks. (30%)
 Lab. (20%)
Textbook
 J. Han, M. Kanber, "Data Mining: Concepts and Techniques", 2000,
Morgan Kauffman Publishers, ISBN: 1-55860-489-8.
Reference Books
1. Margaret H. Dunham "Data Mining: Introductory and Advanced
Topics", Prentice Hall; 1st edition, 2002, ISBN: 0130888923.
2. M.Humphies, M.W.Hawkins, M. C.Dy. Harrys, "Data Warehousing:
Architecture and Implementation", Prentice Hall, 1999, ISBN:
0130809020. 0262232138.