Cluster Analysis
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Course Objectives
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| The aim is to understand the mathematical principles of clustering algorithms and to use them in applications. |
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Course materials
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- Clustering, R. Xu, D. Wunsch, John Wiley & Sons, 2008.
- Data Mining: Concepts and Techniques, J. Han, M. Kamber, J. Pei, Elsevier 2006.
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Assessment
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40% Midterm exam + 60% Final exam
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Prerequisites
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there is no formal prerequisite.
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| Week |
Subjects |
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| 1. |
Introduction to data clustering |
Lesson 1 |
| 2. |
Partitioning clustering algorithms |
Lesson 2 |
| 3. |
Hierarchical clustering algorithms |
Lesson 3 |
| 4. |
Density based clustering algorithms |
Lesson 4 |
| 5. |
Grid based clustering algorithms |
Lesson 5 |
| 6. |
Cluster Validation |
Lesson 6 |
| 7. |
Review for midterm exam |
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| 8. |
Midterm exam |
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| 9. |
Supervised clustering and classification |
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| 10. |
Clustering in time series and discretization |
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| 11. |
Image segmentation by clustering |
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| 12. |
Graph clustering |
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| 13. |
Students Presentations 1 |
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| 14. |
Students Presentations 2 |
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| 15. |
Review for final exam |
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Final Exam |
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