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Information
| Code | CENG552 |
| Name | Data Mining |
| Term | 2022-2023 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 | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. SELMA AYŞE ÖZEL |
Course Goal / Objective
Identifying basic data mining methods and applying to basic problems.
Course Content
Overview of data mining methods including supervised and unsupervised methods. Case studies.
Course Precondition
Programming knowledge to make data preprocessing is required.
Resources
Jiawei Han, Micheline Kamber, and Jian Pei, “Data Mining: Concepts and Techniques” 3rd edition, Morgan Kaufmann, 2011.
Notes
Related recent papers
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Identifies basic data mining methods. |
| LO02 | Explains how to use basic data mining methods. |
| LO03 | Uses basic data mining methods to solve problems. |
| LO04 | Applies basic data mining methods to a real life problem and solves the problem. |
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. | |
| 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. | 3 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 2 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 4 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | |
| 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. | 3 |
| 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. | 1 |
| 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. | 5 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to data mining | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Data preprocessing methods | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | OLAP and data cubes | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Association rule mining algorithms | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Selection criteria for association rules | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Introduction to supervised learning (decision trees, naive bayes) | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Rule based methods, associative classification, lazy learners | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Mid-Term Exam | Reading of course notes | Ölçme Yöntemleri: Ödev, Sözlü Sınav |
| 9 | Perfromance evaluation of classifiers | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Introduction to unsupervised learning (k-means, k-medoids algorithms) | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Hierarchical methods, density based methods | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Grid based methods | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 13 | Performance analysis of clustering methods | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Paper presentations | Literature survey, preparing presentation | Öğretim Yöntemleri: Bireysel Çalışma, Örnek Olay, Soru-Cevap |
| 15 | Project presentations | Presentation preparation | Ölçme Yöntemleri: Sözlü Sınav, Proje / Tasarım |
| 16 | Writing the project report | Preparing the project report | Ölçme Yöntemleri: Sözlü Sınav, Proje / Tasarım |
| 17 | Term Exams | Preparing the project report | Ölçme Yöntemleri: Sözlü Sınav, Proje / Tasarım |
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 | ||