CENG014 Cluster Analysis

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG014
Name Cluster Analysis
Term 2024-2025 Academic Year
Semester . Semester
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 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

Update Time: 24.05.2024 05:00