Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
LOG 5018	DATA MINING	ELECTIVE	3	0	0	8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Logistics Engineering (Non-Thesis-Evening)
Logistics Engineering

Course Objective

The aim of this course is to give students the theoretical background of data mining algorithms and techniques and to give the student the ability to select and apply appropriate data mining techniques for different applications. This course will enable a student to learn data preprocessing, association rule mining, classification and prediction, and cluster analysis with applications.

Learning Outcomes of the Course Unit

1	Define basic data mining concepts
2	Apply preprocessing operations on data
3	Determine which data mining technique is appropriate to solve a particular problem
4	Design a data mining model
5	Implement a data mining algorithm

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Data Mining: definition, motivation, concepts and techniques
2	Data preprocessing: data sampling, data cleaning
3	Data preprocessing: Feature Selection and Dimension Reduction
4	Data Mining Software: Weka
5	Tree-based, rule-based and instance-based methods, Bayes' Theorem-Based Method
6	Tree-based, rule-based and instance-based methods, Bayes' Theorem-Based Method
7	Neural Networks, Linear Differential Function Analysis, Support Vector Machines
8	Community Methods and Model Evaluation
9	Data Mining Software: Weka
10	Apriori Algorithm and Extensions, Pattern Review
11	Apriori Algorithm and Extensions, Pattern Review
12	Partial and Hierarchical Clustering Methods, Graph-Based and Density-Based Clustering Methods, Clustering Performance Evaluation
13	Data Mining Applications in Business
14	Presentations

Recomended or Required Reading

Textbook(s): Han, J. & Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, Second Edition, 2006.

Supplementary Book(s):
1. Roiger, R.J., & Geatz, M.W., Data Mining: A Tutorial-Based Primer, Addison Wesley, USA, 2003.
2. Dunham, M.H., Data Mining: Introductory and Advanced Topics, Prentice Hall, New Jersey, 2003

Planned Learning Activities and Teaching Methods

Lectures,Research,Application Development,Presentation, Term project

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MTE	MIDTERM EXAM
2	ASG	ASSIGNMENT
3	FIN	FINAL EXAM
4	FCG	FINAL COURSE GRADE	MTE * 0.30 +ASG * 0.20 +FIN * 0.50
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	MTE * 0.30 + ASG * 0.20 + RST * 0.50

*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Course outcomes will be evaluated with the presentation of the student about a topic and project / report prepared by the student.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	13	3	39
Preparations before/after weekly lectures	12	6	72
Preparation for midterm exam	1	10	10
Preparation for final exam	1	10	10
Preparing assignments	3	10	30
Preparing presentations	1	20	20
Reading	5	3	15
Final	1	2	2
Midterm	1	2	2
TOTAL WORKLOAD (hours)			200

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.2	PO.3	PO.4	PO.5	PO.6	PO.7	PO.8	PO.9	PO.10	PO.11	PO.12	PO.13
LO.1	3	5	5	5	4	5	5	5	3	5	4	5	5
LO.2	5	5	5	5	4	5	5	5	3	5	5	4	5
LO.3	3	5	5	4	5	4	5	5	5	5	3	5	5
LO.4	5	5	5	5	3	5	5	4	5	5	3	5	4
LO.5	5	5	5	5	3	5	5	4	5	5	5	4	5

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION