COURSE UNIT TITLE

: FUZZY CLUSTERING AND CLASSIFICATION TECHNIQUES

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSC 5006 FUZZY CLUSTERING AND CLASSIFICATION TECHNIQUES 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

PROFESSOR EFENDI NASIBOĞLU

Offered to

Computer Science
Ph.D. in Computer Science

Course Objective

This course aims to provide the students with an overview of the fuzzy clustering and fuzzy classification problems. It also aims to handle the key computational techniques of prototype based and neighborhood based fuzzy clustering and classification models.

Learning Outcomes of the Course Unit

1   Understanding both theoretical and practical knowledge of fuzzy classification and clustering problems.
2   Understanding of the basic mathematical techniques used for processing fuzzy information in clustering and classification process.
3   Understanding of the basic decision making techniques with fuzzy clustering and classification.
4   Understanding of the basic applied tools used in fuzzy clustering and classification.
5   Having ability to develop a practical fuzzy system with clustering and classification.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Fuzzy Clustering and Classification.
2 K-Means vs. Fuzzy C-Means methods.
3 Project 1
4 DBSCAN and FN-DBSCAN Algorithms.
5 Project 2
6 Fuzzy Joint Points (FJP) and Noise Robust- FJP (NR-FJP) Clustering Algorithms
7 Modified FJP and Optimal FJP algorithms.
8 Midterm exam
9 Fuzzy Decision Trees.
10 K- Nearest Neighborhood (KNN) and Fuzzy KNN
11 Project 3
12 C x K-Nearest Neighborhood (C x KNN) Algorithm
13 OWA distance, OWA Based C x KNN
14 Project 4

Recomended or Required Reading

Textbook(s): Höppner F., Klawonn F., Kruse R., Runkler T., Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, Wiley, 1999.
Supplementary Book(s): Larose D., Discovering knowledge in data: An introduction to data mining, J. Wiley & Sons, 2005.
Recent literature papers.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 PRJ PROJECT
2 MTE MIDTERM EXAM
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE PRJ * 0.30 + MTE * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) PRJ * 0.30 + MTE * 0.30 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

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 13 4 52
Preparation for midterm exam 1 20 20
Preparation for final exam 1 20 20
Preparing assignments 2 20 40
Preparing presentations 2 15 30
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 205

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15555
LO.25555
LO.35555
LO.45555
LO.55555

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

Copyright 2015 DEÜ. BİD

HOME / ABOUT US / DEGREE PROGRAMMES / GENERAL INFORMATION FOR STUDENTS / INTERNATIONAL RELATIONS