| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits | Last Updated Date |
| 1 | EE465 | DATA MINING | 3+0+0 | 3 | 5 | 21.08.2025 |
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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
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ELECTRICAL-ELECTRONICS ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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1) To understand the steps of knowledge discovery from data.
2) To explain fundamental pattern discovery techniques.
3) To use classification and clustering algorithms on data mining software.
4) To describe performance evaluation of classification methods.
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Course Content
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This course introduces the fundamentals of data mining. It covers the following topics: Introduction to data mining, data representation techniques, data preprocessing techniques, fundamental pattern discovery techniques such as frequent itemset and association rule mining, and basic concepts of classification and clustering algorithms. Through a course project, the students will use some data mining tools and apply the concepts to a real problem.
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Course Methods and Techniques
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-
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Prerequisites and co-requisities
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None
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Course Coordinator
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None
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Name of Lecturers
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Asist Prof.Dr. BEKİR HAKAN AKSEBZECİ hakan.aksebzeci@agu.edu.tr
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
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Introduction to Data Mining
Data Representation and Data Preprocessing
Pattern Discovery
Basic Concepts and Methods of Classification
Basic Concepts and Methods of Clustering
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
ECTS Allocated Based on Student Workload
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Activities
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Total Work Load
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Yazılı Sınav
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1
|
20
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20
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F2F Dersi
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1
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2
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2
|
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Sunum için Hazırlık
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1
|
10
|
10
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Proje
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1
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20
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20
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Okuma
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1
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2
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2
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Araştırma
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1
|
5
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5
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Takım/Grup Çalışması
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1
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2
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2
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Total Work Load
| |
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Number of ECTS Credits 2
61
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Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
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1
| Describe the techniques that are used to represent data and knowledge. |
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2
| Compare the data transformation techniques. |
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3
| Explain frequent itemset detection and association rule mining methods. |
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4
| Interpret the fundamental classification and clustering methods. |
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5
| Perform data mining methods to a real problem using a data mining software. |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Introduction to Data Mining
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| 2 |
Data Representation
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| 3 |
Data Representation
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| 4 |
Data Preprocessing
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| 5 |
Data Preprocessing
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| 6 |
Midterm Exam - 1
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| 7 |
Pattern Discovery
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| 8 |
Pattern Discovery
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| 9 |
Basic Concepts and Methods of Classification
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| 10 |
Midterm Exam - 2
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| 11 |
Basic Concepts and Methods of Classification
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| 12 |
Techniques to Improve Classification Accuracy
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| 13 |
Basic Concepts and Methods of Clustering
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| 14 |
Term Project Presentations
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| 15 |
Final Exam
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| 16 |
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Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=79006&lang=en