CENG708 Advanced Topics in Data Mining

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

Information

Code CENG708
Name Advanced Topics in 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 Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. SELMA AYŞE ÖZEL


Course Goal / Objective

Learning advanced data mining methods and applying to advanced level problems.

Course Content

Scalable algorithms, flexible predictive modeling, web mining, text and document clustering, automated recommender systems, pattern-finding algorithms. It is assumed that every student is familiar with the basic data mining topics (clustering, classification, and association rules) and has some experience with programming and one or more data mining tools (R, RapidMiner, Weka, XLMiner, etc.).

Course Precondition

Student is expected to know basic data mining techniques and be able to implement programs about the topic.

Resources

Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd Edition, Morgan Kaufmann, 2012.

Notes

Related recent papers


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Explains advanced level data mining methods.
LO02 Decides which data mining technique to apply to solve a problem.
LO03 Applies the advanced level data mining methods to solve problems.
LO04 Develops a data mining application that can solve a real life 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. 4
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. 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. 1
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. 2
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. 3
PLO12 Bilgi - Kuramsal, Olgusal Observes social, scientific and ethical values in all professional activities.


Week Plan

Week Topic Preparation Methods
1 Overview of basic data mining topics Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
2 Advanced pattern mining algorithms Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 Sequence mining algorithms Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
4 Advanced classification algorithms (Support vector machines, Baysian Belief Networks) Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
5 Classificaiton by backpropoagation, other methods Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
6 Advanced clustering methods (Probabilistic model-based clustering) Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
7 Clustering high dimensional data Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
8 Mid-Term Exam Reading of course notes, literature review Ölçme Yöntemleri:
Sözlü Sınav, Ödev
9 Clustering graph and network data Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
10 Outlier detection methods Reading of course notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
11 Data mining trends (web mining) Literature survey Öğretim Yöntemleri:
Anlatım, Soru-Cevap
12 Text mining, Recommender systems Literature survey Öğretim Yöntemleri:
Tartışma, Bireysel Çalışma
13 Social network analysis Literature survey Öğretim Yöntemleri:
Tartışma, Bireysel Çalışma
14 Paper presentations Literature survey, preparing presentation Öğretim Yöntemleri:
Bireysel Çalışma, Örnek Olay, Soru-Cevap
15 Project presentations Preparing project and its presentation Öğretim Yöntemleri:
Proje Temelli Öğrenme , Tartışma, Soru-Cevap
16 Writing the project report Preparing the project report Ölçme Yöntemleri:
Sözlü Sınav, Proje / Tasarım
17 Term Exams Submission of the project report Ölçme Yöntemleri:
Proje / Tasarım, Sözlü 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