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| YZM4008 | Data Mining | 2+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Prof. Dr. Hamdi Tolga KAHRAMAN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to provide students with theoretical knowledge about data mining algorithms and techniques and to give students the ability to select and implement appropriate data mining techniques for different applications. This course is designed for students; data preprocessing, association rule analysis, classification and estimation, and applications and clustering analysis. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | To be able to define basic data mining concepts | 1,4,5,8 | 1 | | LO - 2 : | Gain the ability to find and extract useful information from the data sets | 1,4,5,8 | 1 | | LO - 3 : | Identify the appropriate data mining technique to solve a specific problem | 1,4,5,8 | 1 | | LO - 4 : | Gaining knowledge and skill of classification and clustering with methods with and without supervisor | 1,4,5,8 | 1,6 | | LO - 5 : | Designing a data mining model and developing applications | 1,4,5,8 | 1,6 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Introduction to data mining, data mining definitions, data preparation, data mining techniques, classification, decision trees, association rules, clustering. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Research and Application Project Presentation | | | Week 2 | Data warehouses, data set repositories (UCI Machine Learning Data Repository), data preparation, research and application project | | | Week 3 | Data mining process and data mining techniques | | | Week 4 | Statistical Classification (Naive Bayes), Research and Application Project Control | | | Week 5 | Classification techniques (k-nearest neighbor algorithm) and its application, Research and Application Project Control | | | Week 6 | Classification techniques (decision tress) and its application, Research and Application Project Control | | | Week 7 | Classification techniques (artificial neural networks) and its application, Research and Application Project Control | | | Week 8 | Clustering Techniques, Hierarchical and Non-hierarchical Techniques, Research and Application Project Control | | | Week 9 | Mid-Term Exam | | | Week 10 | Clustering by K-means methods, Research and Application Project Control | | | Week 11 | Clustering with the farthest and nearest neighbors, Research and Application Project Control | | | Week 12 | Association Rules and Apriori Algorithm, Research and Application Project Control | | | Week 13 | Research and Application Project Presentation | | | Week 14 | Research and Application Project Presentation | | | Week 15 | Research and Application Project Presentation | | | Week 16 | Final Exam | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 70 | 25 | | Project | 13 | | 120 | 25 | | End-of-term exam | 16 | | 70 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 2 | 14 | 28 | | Sınıf dışı çalışma | 3 | 12 | 36 | | Arasınav için hazırlık | 1 | 8 | 8 | | Arasınav | 2 | 1 | 2 | | Proje | 2 | 8 | 16 | | Dönem sonu sınavı için hazırlık | 2 | 5 | 10 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 102 |
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