COURSE UNIT TITLE

: DATA MINING

DEGREE PROGRAMMES

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


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/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.13555455545553
LO.25555545535545
LO.35555455354553
LO.43554444555555
LO.55553545535455

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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