CENG552 Data Mining

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

Information

Code CENG552
Name Data Mining
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 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

Update Time: 13.05.2024 03:07