COURSE UNIT TITLE

: APPLIED DATA MINING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MIF 5013 APPLIED DATA MINING ELECTIVE 2 0 0 5

Offered By

Medical Informatics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR EFENDI NASIBOĞLU

Offered to

Medical Informatics

Course Objective

The objective of the course is to learn basic methods and application areas of data mining and to gain skills regarding application of these methods on computer.

Learning Outcomes of the Course Unit

1   The student should define basic concepts of data mining.
2   The student should analyze the data with the use of basic data mining methods.
3   The student should apply data analysis methods on computer.
4   The student should determine the appropriate data mining method for clinical studies.
5   The student should interpret the results.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Data Mining
2 Process of CRISP-DM
3 Data overhead operation
4 Explanatory data analysis
5 Statistical methods for estimation and prediction
6 K-nearest neighbor algorithm
7 Decision trees
8 Midterm
9 Artificial neural networks
10 Hierarchical clustering
11 k-means clustering
12 Fuzzy clustering
13 Kohonen neural networks
14 Association rules / Model evaluation techniques

Recomended or Required Reading

1. Larose D., Discovering Knowledge in Data: An introduction to data mining, John Wiley & Sons, Inc., 2005.
2. Han, J., Kamber, M., Data Mining: Concepts and techniques, Ed. Morgan Kaufmann, 2006.
3. Pal, S.K., Mitra, P. Pattern Recognition Algorithms for Data Mining, Chapman&Hall/CRC, 2004.

Planned Learning Activities and Teaching Methods

Problem analysis, design and application, presentation/lecturing and interactive discussion.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 LAB LABORATORY
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE VZ * 0.25 + LU * 0.25 + FN* 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homeworks, projects and exams.

Language of Instruction

Turkish

Course Policies and Rules

Attendance is an essential requirement of this course and is the responsibility of the student. Students are expected to attend all lecture and recitation hours. Attendance must be at least 70% for the lectures.

Contact Details for the Lecturer(s)

Dokuz Eylül University, Faculty of Science, Department of Computer Science
e-mail: efendi.nasiboglu@deu.edu.tr
Tel: 0232 412 85 52

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 15 2 30
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Preparing assignments 1 22 22
Preparing presentations 1 15 15
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 127

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15PO.16
LO.15455
LO.25545
LO.34545
LO.45555
LO.54555

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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