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| END4012 | Introduction to Data Mining | 3+0+0 | ECTS:5 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of INDUSTRIAL ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Doç. Dr. Hüseyin Avni ES | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to provide the students with the theoretical knowledge of data mining algorithms and techniques and to give the students the ability to select and apply appropriate data mining techniques for different applications. This course is designed for students; learn pre-processing, association rule analysis, classification and estimation and clustering analysis. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Gain the ability to find and extract useful information from data sets | 2 | 1,6 | | LO - 2 : | To determine the appropriate data mining technique to solve a specific problem | 5 | 1,6 | | LO - 3 : | Designing and developing a data mining model and application | 11 | 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 | Introduction to Data Mining, Terminology of Data Mining and Definitions | | | Week 2 | Data repository, data sets (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 trees), Research and Application Project Control | | | Week 7 | Classification techniques (artificial neural networks) and application, Research and Application Project Control | | | Week 8 | Clustering Techniques, Hierarchical and Non-hierarchical Techniques, Research and Implementation Project Control | | | Week 9 | Mid-term exam | | | Week 10 | Clustering with K-means | | | Week 11 | Clustering with the most remote and closest neighboring methods | | | Week 12 | Association Rules and Apriori Algorithm | | | Week 13 | Presentation of Research and Application Projects | | | Week 14 | Presentation of Research and Application Projects | | | Week 15 | Presentation of Research and Application Projects | | | Week 16 | Final Exam | | | |
| 1 | Silahtaroglu, G., Veri Madenciligi, ISBN: 978-975-6797-81-5, 3. Basim, 2016, 304 sayfa, Papatya | | | 2 | Özkan, Y., Veri Madenciligi Yontemleri, ISBN: 978-975-6797-82-2, 3. Basim, 2016, 240 sayfa, Papatya | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 6/04/2019 | 1 | 30 | | Project | 13 | 14/05/2019 | 1 | 20 | | End-of-term exam | 16 | 20/05/2019 | 1 | 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 | 15 | 30 | | Sınıf dışı çalışma | 2 | 15 | 30 | | Arasınav için hazırlık | 1 | 8 | 8 | | Arasınav | 1 | 1 | 1 | | Uygulama | 1 | 11 | 11 | | Proje | 2 | 12 | 24 | | Dönem sonu sınavı için hazırlık | 1 | 15 | 15 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 120 |
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