TÜRKÇE
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Information
| Code | ZO018 |
| 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 | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ZEYNEL CEBECİ |
| Course Instructor |
1 |
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 | ||