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| TBB6001 | Advanced Data Mining | 2+2+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Compulsory | | Department | DEPARTMENT of BIOSTATISTICS and MEDICAL INFORMATICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Group study | | Contact Hours | 14 weeks - 2 hours of lectures and 2 hours of practicals per week | | Lecturer | -- | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Be able to approach data mining as a process | | | | PO - 2 : | Be proficient with leading data mining software, including WEKA, Clementine by SPSS, Statistica | | | | PO - 3 : | Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, Classificatin and regression trees, the C4.5 algortihm, logistic Resression, k-nearest neighbor, multiple regression, network | | | | PO - 4 : | Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues. | | | | PO - 5 : | Understand and apply data mining techniques and application for medical data. | | | | 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), PO : Learning Outcome | | |
| Using primary and secondary data sources, data representation, and data analysis principles, data warehousing, information management policies
Data, information, konwledge, software, hardware, computers, networks, information systems management as well as basic computer terminology data mining techniques(Association Rules, Classificaiton, Clustering, Decision Tress and other machine learning models)
Health data mining applications
Data mining software tools(SPSS, Statistics for Oracle, etc.)
Homework projects and students presentations. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Classification and Prediction | | | Week 2 | Classification by Decision Tree Induction | | | Week 3 | Bayesian Classification | | | Week 4 | Rule-Base Classification | | | Week 5 | Support Vector Machines | | | Week 6 | Associative Classification and Lazy Learns Methods | | | Week 7 | Midterm Exam | | | Week 8 | Cluster Analysis | | | Week 9 | Meaning Stream, Time-Series and Squence Data | | | Week 10 | Graph Mining, Social Network Analysis, and Multirelational Data Mining | | | Week 11 | Mining Objects, Spatial, Multimedia, Text, and Web Data | | | Week 12 | Applications and Trends in Medical Data Mining | | | Week 13 | Project Presentation | | | Week 14 | Final Exam | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 7 | | 2 | 50 | | End-of-term exam | 14 | | 2 | 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 | 3 | 11 | 33 | | Arasınav | 2 | 1 | 2 | | Ödev | 2 | 3 | 6 | | Proje | 10 | 14 | 140 | | Dönem sonu sınavı için hazırlık | 5 | 1 | 5 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 188 |
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