|
|
| BIL4008 | Data Mining | 3+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Çağatay Murat YILMAZ | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To teach students basic concepts of data mining and popular methods; gain the ability to select the right data mining tool in real world problems |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Have knowledge about basic concepts of data mining | 2,3,4,12 | 1 | | LO - 2 : | Learn the popular methods used in data mining | 2,3,4,12 | 1 | | LO - 3 : | Have the ability to choose the right data mining tool in real world problems | 2,3,4,12 | 1 | | 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 | | |
| Basic concepts, preparing data, data reduction, distribution based clustering, decision trees, ensemble learning, clustering analysis, association rules, web and text mining, graph mining, temporal and spatial data mining, visualization methods |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Data Mining Concepts: Data Mining Process, Data Warehouses, Data Marts, Large Data Sets
| | | Week 2 | Preparing the Data: Representation of Raw Data, Characteristics of Raw Data, Transformation of Raw Data | | | Week 3 | Missing Data, Time Dependent Data, Outlier Analysis | | | Week 4 | Data Reduction: Feature Reduction, Relief Algorithm, Entropy Measure for Ranking Features, PCA | | | Week 5 | Value Reduction, Feature Discretization: ChiMerge Technique, Case Reduction | | | Week 6 | Learning From Data: Support Vector Machines, k-NN, model selection vs generalization | | | Week 7 | Bayesian Classification, Logistic Regression, LDA | | | Week 8 | Decision Trees | | | Week 9 | Midterm exam
| | | Week 10 | Ensemble Learning: Bagging, Boosting, AdaBoost | | | Week 11 | Cluster Analysis: DBSCAN, DENCLUE | | | Week 12 | Association Rules: Apriori, FP Growth | | | Week 13 | Web Mining, Text Mining | | | Week 14 | Graph Mining, Temporal Data Mining, Spatial Data Mining | | | Week 15 | Visualization Methods | | | Week 16 | Final exam | | | |
| 1 | Data Mining - Concepts, Models, Methods, and Algorithms - Mehmed Kantardzic, 2nd edition, Wiley, 2011, 534 pages | | | |
| 1 | Data Mining: Concepts and Techniques 3rd edition - Jiawei Han, Micheline Kamber, Jian Pei, Morgan Kaufmann, 2012, 744 pages. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | | 50 | | End-of-term exam | 16 | | | 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 | 14 | 42 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Arasınav için hazırlık | 6 | 1 | 6 | | Arasınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 6 | 1 | 6 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 100 |
|