Information Package / Course Catalogue
Data Mining
Course Code: YBS406
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 1
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

Data warehouse architecture, data mining models, algorithms must be applied in a tab is to teach

Course Content

Introduction to Data Mining, Data Preparation Methods of Classification, Clustering Methods, Association Rules, Text Mining, Web Mining

Name of Lecturer(s)
Assoc. Prof. Pınar Zarif TAN
Learning Outcomes
1.Generates the data warehouse database
2.Data Mining Model to associate with each other.
3.Examines the classification model and apply
4.Examines the clustering model and apply
5.Analysis examines the connection model and apply
6.Data Mining Algorithms are applied
Recommended or Required Reading
1.Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Weekly Detailed Course Contents
Week 1 - Theoretical
Data Warehousing and Data Mining Concepts
Week 1 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 2 - Theoretical
Veri Madenciligi Modelleri
Week 2 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 3 - Theoretical
classification
Week 3 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 4 - Theoretical
Decision Trees
Week 4 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 5 - Theoretical
Classification in Data Mining Applications
Week 5 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 6 - Theoretical
Clustering Model
Week 6 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 7 - Theoretical
Artificial Neural Networks
Week 8 - Theoretical
Genetic Algorithms
Week 9 - Theoretical
Clustering Method to be applied for different data sets
Week 9 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 10 - Theoretical
Linkage Analysis Model
Week 10 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 11 - Theoretical
Interpretation of Data Mining applications
Week 11 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 12 - Theoretical
Remove and interpretation of rules
Week 12 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 13 - Theoretical
Remove and interpretation of rules
Week 13 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Week 14 - Theoretical
Course Assessment
Week 14 - Preparation Work
Jiawei Han and Micheline Kamber (2006)., Data Mining: Consept and Techniques
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory132252
Lecture - Practice130113
Assignment132026
Individual Work132026
Quiz24110
Midterm Examination19110
Final Examination112113
TOTAL WORKLOAD (hours)150
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
PÇ-15
OÇ-1
1
2
2
3
4
2
3
3
3
3
3
3
2
3
4
OÇ-2
2
3
3
3
3
4
4
4
4
3
3
4
3
3
4
OÇ-3
2
1
4
4
2
2
3
3
2
4
4
4
4
4
5
OÇ-4
4
4
4
3
3
2
2
3
3
4
4
2
1
3
4
OÇ-5
2
3
3
4
4
4
4
5
5
5
5
5
5
4
3
OÇ-6
2
3
4
4
3
4
5
5
5
4
4
3
3
4
4
Adnan Menderes University - Information Package / Course Catalogue
2026