MATHEMATICS (TURKISH, PHD)
PhD TR-NQF-HE: Level 8 QF-EHEA: Third Cycle EQF-LLL: Level 8

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP6021 Data Mining and Knowledge Discovery Fall 3 0 3 12
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. ADEM KARAHOCA
Course Lecturer(s): Prof. Dr. ADEM KARAHOCA
Course Objectives: The objectives of this course are applying data mining solutions on knowledge discovery areas, providing suitable data mining models for real life cases, giving an overview of the subjects and offering data mining solutions will provide an in-depth coverage for data mining and knowledge discovery.

Learning Outputs

The students who have succeeded in this course;
1. Analyze data mining and knowledge discovery process model.
2. Analyze rough set theory.
3. Analyze clustering and classification in data mining.
4. Analyze rule mining.
5. Benchmark data mining algorithms.
6. Identify data mining in web applications.
7. Analyze applications of data mining.

Course Content

The content of this course consists of Data Mining & Knowledge Discovery Process Model, Knowledge Discovery on the Grid, Rough Set Theory — Fundamental Concepts, Principals, Data Extraction, and Applications, Hybrid Clustering for Validation and Improvement of Subject-Classification Schemes, Hyperspectral Remote Sensing Data Mining Using Multiple Classifiers Combination, Content-based Image Classification via Visual Learning, Mining Multiple-level Association Rules Based on Pre-large Concepts, Benchmarking the Data Mining Algorithms with Adaptive Neuro-Fuzzy Inference System in GSM Churn Management, A Novel Model for Global Customer Retention Using Data Mining Technology, Data Mining in Web Applications, Quality Improvement using Data Mining in Manufacturing Processes, The Deployment of Data Mining into Operational Business Processes, An Overview of Data Mining Techniques Applied to Power Systems.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) A Data Mining & Knowledge Discovery Process Model
2) Knowledge Discovery on the Grid
3) Rough Set Theory — Fundamental Concepts, Principals, Data Extraction, and Applications
4) Hybrid Clustering for Validation and Improvement of Subject-Classification Schemes
5) Hyperspectral Remote Sensing Data Mining Using Multiple Classifiers Combination
6) Content-based Image Classification via Visual Learning
7) Mining Multiple-level Association Rules Based on Pre-large Concepts
8) Mining Multiple-level Association Rules Based on Pre-large Concepts / Midterm
9) Benchmarking the Data Mining Algorithms with Adaptive Neuro-Fuzzy Inference System in GSM Churn Management
10) A Novel Model for Global Customer Retention Using Data Mining Technology
11) Data Mining in Web Applications
12) Quality Improvement using Data Mining in Manufacturing Processes
13) The Deployment of Data Mining into Operational Business Processes
14) An Overview of Data Mining Techniques Applied to Power Systems

Sources

Course Notes: Data Mining and Knowledge Discovery in Real Life Applications Edited by: Julio Ponce and Adem Karahoca, 2009
References: Yok

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 14 % 10
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes 4 % 10
Homework Assignments 2 % 10
Presentation % 0
Project % 0
Seminar % 0
Midterms 1 % 30
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
Total % 100

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution