Dr. Mohammad Ali H. Eljinini

Course Name: Special Topics in Computer Science - Data Mining

Course Number: 605399

Course Description:

(3 credit hours, Prerequisite: 605223 and 605311)

Introduction to data mining, Input Concepts, Knowledge Representation, Decision Tables, Decision Trees, Classification Rules, Association Rules, Data Mining Algorithms and Implementations in Java.

Course Contents:  

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

     . 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

TEXTBOOK:

Data Mining: Practical machine learning tools and techniques with Java implementation.

Ian Witten, Eibe Frank.  Morgan Kaufmann, 2000

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.

EXAMS & GRADES:

 First Exam    25%

Second Exam   25%

Final Exam    40%

Class Activities 10%