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An Najah National University
Information Technology Faculty – CIS Department
Course title
Instructor(s) name(s)
Contact information
Semester/Year
Compulsory / Elective
Prerequisites
Course
Contents
(description)
Course Objectives
Intended learning
Outcomes and
Competences
Textbook and
References
Data Mining
Dr. Fady DRaidi
Email:[email protected]
Office: IT Building, 2nd floor
Second Semester 2013
Elective
Data Mining studies algorithms and computational paradigms that allow
computers to find patterns and regularities in databases, perform prediction and
forecasting, and generally improve their performance through interaction with
data. It is currently regarded as the key element of a more general process called
Knowledge Discovery that deals with extracting useful knowledge from raw data.
The knowledge discovery process includes data selection, cleaning, coding, using
different statistical and machine learning techniques, and visualization of the
generated structures. The course will cover all these issues and will illustrate the
whole process by examples
 Present fundamental concepts and techniques for data mining
 Provide necessary background for applying data mining to business problems
 Conduct case studies on real data mining examples
 Practice data mining tools on real data
Upon successful completion of the course the student will:
 Be able to understand the concepts, strategies, and methodologies related to
the design and construction of data mining
 Be able to comprehend several data preprocessing methods
 Be able to utilize data warehouses and OLAP for data mining and knowledge
discovery activities
 Be able to determine an appropriate mining strategy for given large dataset
 Be able to apply appropriate mining techniques to extract unexpected patterns
and new rules that are "hidden" in large databases
 Be able to obtain knowledge of current data mining applications
Introduction to Data Mining, P.N. Tan, M. Steinbach, V. Kumar
Visit the book webpage at www.cs.umn.edu/~kumar/dmbook
 Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and
Techniques, 3rd ed
 E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011.
 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical
Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer-Verlag,
2009.
 T. M. Mitchell, Machine Learning, McGraw Hill, 1997.
 P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining,
Addison Wesley, 2005.
 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools
and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed
1
Assessment Criteria
Activity
First Exam
Second Exam
Homework and quizzes
Date
Final Exam
Percent (%)
15
15
30
40
Subject
1
2
3
4
5
7
9
Introduction
Data and Data Exploration
Classification Algorithms
Association Analysis
Clustering
Anomaly Detection
Special lectures
2