MATHEMATICS (TURKISH, PHD) | |||||
PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 |
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 |
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. |
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. |
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. |
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 |
Course Notes: | Data Mining and Knowledge Discovery in Real Life Applications Edited by: Julio Ponce and Adem Karahoca, 2009 |
References: | Yok |
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 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution |