TÜRKÇE
ENGLISH
Information
| Code | CENG014 |
| Name | Cluster Analysis |
| Term | 2024-2025 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. UMUT ORHAN |
| Course Instructor |
1 |
Course Goal / Objective
The aim is to understand the mathematical principles of clustering algorithms and to use them in applications.
Course Content
Partitioning-Hierarchical-Density based-Grid based clustering algorithms, Cluster Validation, Supervised clustering and classification, Clustering in time series and discretization, Image segmentation by clustering, Graph clustering
Course Precondition
none
Resources
Clustering, R. Xu, D. Wunsch, John Wiley & Sons, 2008. Data Mining: Concepts and Techniques, J. Han, M. Kamber, J. Pei, Elsevier 2006.
Notes
Articles
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Knows partitioning-Hierarchical-Density based-Grid based clustering algorithms |
| LO02 | Do cluster validation for a special dataset |
| LO03 | Knows Supervised clustering and classification approaches |
| LO04 | Apply clustering methods to 1-D ve 2-D data |
| LO05 | Knows graph clustering concept |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 3 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 3 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 3 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | 2 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 2 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 2 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to data clustering | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 2 | Partitioning clustering algorithms | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 3 | Hierarchical clustering algorithms | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 4 | Density based clustering algorithms | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 5 | Grid based clustering algorithms | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | Cluster Validation | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Review for midterm exam | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 8 | Mid-Term Exam | Study to lecture notes and applications | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Supervised clustering and classification | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 10 | Clustering in time series and discretization | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 11 | Image segmentation by clustering | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Graph clustering | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Clustering samples in some real world problems | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 14 | Students Projects and Presentations | Preparation an application and a presentation for chosen project | Öğretim Yöntemleri: Proje Temelli Öğrenme |
| 15 | Review for final exam | Reading related chapter in lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme |
| 16 | Term Exams | Study to lecture notes and applications | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Study to all lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||