Data Warehouses and Data Mining(INF 511)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| INF 511 | Data Warehouses and Data Mining | 1 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Masters Degree |
| Course Instructor(s) | Günce Keziban ORMAN korman@gsu.edu.tr (Email) |
| Assistant | |
| Objective | This class aims at introducing the data mining process to students. This includes the description of data preparation and preprocessing, of various data mining algorithms and of the tools available to assess their results. The class focuses on standard approaches regarding association rules mining, supervised classification and unsupervised classification (clustering). Basic statistical knowledge is necessary to understand the mining algorithms and the quality assessment tools. |
| Content |
- data pre-processing - supervised classification - clustering - complex data mining - results validation and quality assessment |
| Course Learning Outcomes |
1. Data preparation 2. Theoretical and practical knowledge of standard data mining algorithms 3. Standard assessment tools |
| Teaching and Learning Methods |
theoretical & practical class assignments |
| References |
• Data Mining - Practical Machine Learning Tools, 2nd edition, Ian H. Witten & Eibe Frank, Morgan Kaufmann, 2005. • Neural Networks - A Comprehensive Foundation, 2nd edition, Simon Haykin, Pearson/Prentice Hall,1999. • Data Mining: Concepts and Techniques, Jiawei Han & Micheline Kamber, Morgan Kaufmann, 2000. • Applied Statistics and Probabilities for Engineers, 4th edition, D.C. Montgomery & G.C. Runger, John Willey & sons, 2006. • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, T. Hastie, R. Tibshirani & J. Friedman, Springer, 2009. |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Introduction |
| 2 | Data preparation |
| 3 | Association rules and a priori algorithm |
| 4 | FP-trees and complex rules |
| 5 | Decision trees and naïve Bayes classifier |
| 6 | Statistical regression and Bayesian networks |
| 7 | Neural networks and other classifiers |
| 8 | Quality assessment on classification results |
| 9 | Classifier comparison |
| 10 | Distance and partitioning |
| 11 | Hierarchical clustering methods |
| 12 | Clustering with grids and density |
| 13 | Model-based processing |
| 14 | Outliers detection |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 1 | 40 |
| Contribution of final exam to overall grade | 1 | 60 |
| Toplam | 2 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 1 | 40 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 0 | 0 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Toplam | 1 | 40 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | X | |||||
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| 3 | X | |||||
| 4 | X | |||||
| 5 | X | |||||
| 6 | X | |||||
| 7 | X | |||||
| 8 | X | |||||
| 9 | X | |||||
| 10 | X | |||||
| 11 | X | |||||
| 12 | X | |||||
| 13 | X | |||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 12 | 8 | 96 |
| Assignments | 8 | 6 | 48 |
| Final Examinations (including preparation) | 1 | 10 | 10 |
| Total Workload | 196 | ||
| Total Workload / 25 | 7,84 | ||
| Credits ECTS | 8 | ||