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Al-Isra Private University
1
Faculty of Science and Information Technology
Department of Computer Science
Course Plan
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Course No.:
Course Classification:
605399
Elective (CS)
Course Name:
Time Division:
Special Topics in Computer Science 3 Lectures
(Data Mining)
Semester:
Year:
First
2004/2005
Course Description
(3 credit hours, Prerequisites: 605223/Data Structures and 605311/OOP with Java)
Introduction to data mining, Input Concepts, Knowledge Representation, Decision
Tables, Decision Trees, Classification Rules, Association Rules, Data Mining
Algorithms and Implementations in Java.
Course Objectives
At the end of the course, students are expected to learn:
 Data Mining and Machine Learning Concepts and uses
 The different types of Data Mining techniques
 How to decide which technique to use with different problems
 How to implement Data mining methods in the Java Language
Course Outline
Topic
1.Introduction
 Data mining and machine learning
 Simple Examples
 Fielded Applications
 Generalization as Search
2. Input
 Concepts
 Instances
 Attributes
 Preparing the Input
3. Output (Knowledge Representation)
 Decision tables
 Decision trees
 Classification rules
 Association rules
 Rules with exceptions
 Rules involving relations
Textbook
Sections
Ch. 1
Contact
Hours
6
Ch. 2
6
Ch. 3
6
Al-Isra Private University
2
Faculty of Science and Information Technology
Department of Computer Science
Course Plan
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 Trees for numeric prediction
 Instance-based representation
 Clusters
4. Algorithms
 Inferring rudimentary
 Statistical modeling
 Constructing decision trees
 Constructing rules
 Mining association rules
 Linear models
 Instance-based learning
5. Credibility (Evaluating what's been learned)
 Training and testing
 Predicting performance
 Cross-validation
 Other estimates
 Comparing data mining schemes
 Predicting probabilities
 Counting the cost
 Evaluating numeric prediction
 The minimum description length
principle

6. Implementations
 Decision trees
 Classification rules
 Support vector machines
 Instance-based Learning
 Numeric prediction
 Clustering
Ch. 4
15
Ch. 5
3
Ch. 6
3
Textbook
Data Mining: Practical machine learning tools and techniques with Java
implementation.
Ian Witten, Eibe Frank. Morgan Kaufmann, 2000
Suggested references
1. Data Mining, Adriaans, Zantige, Addison-Wesley, 1997.
2. Discovering data mining: From concepts to implementation, Cabena,
Hadjinian, Prentice Hall, 1998.
3. Machine learning, Mitchell, McGraw Hill, 1997.
Al-Isra Private University
3
Faculty of Science and Information Technology
Department of Computer Science
Course Plan
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Marking
First Exam
Second Exam
Activity
Final Exam
25 marks
25 marks
10 marks
40 marks
Assignments and/or Projects
In each major section the students will be given assignments for practicing and
developing a good concept of the topic. Assignments’ deadlines and method of
delivery will be specified by instructors throughout the course.
Required Tools/Software

Java 1.2 or above
Instructors' information
Class: (1) Lecture Room: 4135
Time: 11:15 – 12:35 [Mon, Wed]
Instructor's Name: Eljinini, M.A.
Office Hours: Sun
Tue
Thu
[1:00 – 3:00]
[3:00 – 4:00]
[5:15 – 6:20]
Office No.:4125
Mon
Wed
[1:55 – 2:55]
[12:45 – 1:45]