ZO018 Cluster Analysis

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

Information

Code ZO018
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 Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. ZEYNEL CEBECİ


Course Goal / Objective

This course aims to teach hierarchical agglomerative and divisive clustering methods, soft and crisp partitioning clustering algorithms, and clustering analysis with R.

Course Content

This course includes the topics of clustering terminologies, hierarchical agglomerative and divisive clustering methods, soft and crisp partitioning clustering algorithms, and practical works with R.

Course Precondition

No prerequisites

Resources

Cebeci, Z. (2019). Hierarchical Clustering in Bioinformatics Data Analysis With R. Papatya Bilim Yayınevi, Istanbul. ISBN: 978-605-9594-44-8

Notes

Cebeci, Z et al (2020). ppclust: Probabilistic and Possibilistic Cluster Analysis. R package on CRAN. URL https://cran.r-project.org/package=ppclust


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Acquires the knowledge on the importance and requirements for cluster analysis.
LO02 Have experience to classify the clustering methods and algorithms.
LO03 Understands the needs for hierarchical cluster analysis.
LO04 Learns the hierarchical agglomerative and divisive clustering methods
LO05 Learns visualization of clustering results
LO06 Understands the determination of optimal number of clusters
LO07 Analyse data using hierarchical agglomerative methods with R.
LO08 Analyse data using hierarchical divisive methods with R.
LO09 Learns the partitioning clustering algortihms.
LO10 Analyse data using the partitioning cluster algorithms with R.
LO11 Learns the hard clustering algorithms (K-means and its extensions)
LO12 Learns the soft clustering algorithms (FCM, PCM and their extensions).


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal After undergraduate education, increases knowledge in one of the fields of animal breeding and breeding, feeds and animal nutrition, biometrics and genetics. 3
PLO02 Bilgi - Kuramsal, Olgusal Understands the interaction between different disciplines 2
PLO03 Bilgi - Kuramsal, Olgusal Gains the ability to develop strategic approaches and produce regional, national or international solutions for the field of animal science
PLO04 Bilgi - Kuramsal, Olgusal Zootekni bilimindeki verileri kullanarak bilimsel yöntemlerle bilgiyi geliştirebilme, bilimsel, toplumsal ve etik sorumluluk bilinci ile bu bilgileri kullanabilme becerisini kazanır 2
PLO05 Bilgi - Kuramsal, Olgusal Gains the ability to use and develop information technologies with computer software and hardware knowledge required by the field of animal science. 5
PLO06 Bilgi - Kuramsal, Olgusal Gains the ability to convey their own studies or current developments in the field of animal science to groups in the field or other fields of science, verbally and visually.
PLO07 Bilgi - Kuramsal, Olgusal Gains the ability to evaluate the quality processes of animal products
PLO08 Bilgi - Kuramsal, Olgusal Gains the ability to keep animal production dynamic in accordance with changing economic and social conditions.
PLO09 Bilgi - Kuramsal, Olgusal Gains the ability to follow national and international current issues, to follow developments in lifelong learning, science and technology, to constantly renew themselves and to transfer innovations to animal production.
PLO10 Bilgi - Kuramsal, Olgusal Absorbs the relationship between animal products and human health and community welfare


Week Plan

Week Topic Preparation Methods
1 Introduction to cluster analysis On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Soru-Cevap
2 Clustering methods and algorithms On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 Hierarchical clustering methods On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Soru-Cevap
4 Hierarchical agglomerative and divisive clustering methods On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Soru-Cevap
5 Visualization of clustering results On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
6 Determination of optimal number of clusters On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
7 Hierarchical agglomerative clustering with R On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
8 Mid-Term Exam Preparation for the exam Ölçme Yöntemleri:
Ödev, Sözlü Sınav
9 Practical works using Mona and Diana with R On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
10 Partitioning clustering methods On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
11 Hard clustering algorithms (K-means and its extensions) On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
12 Soft clustering algorithms (FCM, PCM and their extensions) On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Tartışma, Alıştırma ve Uygulama, Gösteri, Gösterip Yaptırma
13 Partitioning cluster analysis with R On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
14 Practical works on hard clustering algorithms (K-means and its extensions) On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Alıştırma ve Uygulama
15 Practical works on the soft clustering algorithms (FCM, PCM and their extensions) On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Alıştırma ve Uygulama
16 Term Exams Preparation for the exam Ölçme Yöntemleri:
Ödev, Sözlü Sınav
17 Term Exams Preparation for the exam Ölçme Yöntemleri:
Sözlü Sınav, Ödev


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: 13.05.2024 01:45