CEN481 Introduction to Data Mining

6 ECTS - 3-0 Duration (T+A)- 7. Semester- 3 National Credit

Information

Code CEN481
Name Introduction to Data Mining
Term 2024-2025 Academic Year
Semester 7. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Öğr. Gör.Dr. HAVVA ESİN ÜNAL
Course Instructor Prof. Dr. SELMA AYŞE ÖZEL (A Group) (Ins. in Charge)


Course Goal / Objective

The objective of this course is to introduce basic data mining algorithms.

Course Content

Introduction to data mining, data preprocessing, association rules, classification, clustering, outlier detection algorithms and their applications.

Course Precondition

None

Resources

Jiawei Han, Micheline Kamber and Jian Pei , Data Mining: Concepts and Techniques, 3rd edition, Morgan Koufmann,2011

Notes

Any reference to Weka and Python


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Comprehends the basic data mining algorithms.
LO02 Applies data preprocessing, association rules, classification, clustering, outlier detection algortihms
LO03 Applies data mining to solve up-to-date problems.
LO04 Decides which data mining technique is to be applied in which case.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Adequate knowledge of mathematics, science and related engineering disciplines; ability to use theoretical and applied knowledge in these fields in solving complex engineering problems. 3
PLO02 Bilgi - Kuramsal, Olgusal Ability to identify, formulate and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. 5
PLO03 Bilgi - Kuramsal, Olgusal Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. 3
PLO04 Bilgi - Kuramsal, Olgusal Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively. 3
PLO05 Bilgi - Kuramsal, Olgusal Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics. 4
PLO06 Bilgi - Kuramsal, Olgusal Ability to work effectively in interdisciplinary and multidisciplinary teams; individual working skills. 3
PLO07 Bilgi - Kuramsal, Olgusal Ability to communicate effectively verbally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and understandable instructions. 4
PLO08 Bilgi - Kuramsal, Olgusal Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology, and constantly renew oneself. 3
PLO09 Bilgi - Kuramsal, Olgusal Knowledge of ethical principles, professional and ethical responsibility, and standards used in engineering practice. 3
PLO10 Bilgi - Kuramsal, Olgusal Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development.
PLO11 Bilgi - Kuramsal, Olgusal Knowledge of the effects of engineering practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.


Week Plan

Week Topic Preparation Methods
1 Definition of data mining, and steps of data mining process Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
2 Data preprocessing steps Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 Weka package Reading the lecture notes and making practice Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Gösterip Yaptırma
4 Association rule mining algorithms Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
5 Performance improvements of association rule mining algortihms Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
6 Basic classification algorithms (decision tree, Naive Bayes) Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
7 Classifiers' performance evaluation techniques Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
8 Mid-Term Exam Reading the lecture notes Ölçme Yöntemleri:
Yazılı Sınav
9 Rule based classifiers, SVM and other classifiers Reading the lecture notes and making practice Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Gösterip Yaptırma
10 Basic clustering algorithms (k means) Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
11 Basic clustering algorithms (hierarchical methods) Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
12 Outlier detection methods Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
13 Introduction to Web and text mining Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
14 Preparing the project presentations Making practice, and preparing presentation Öğretim Yöntemleri:
Proje Temelli Öğrenme , Grup Çalışması
15 Project presentations Making practice, and preparing presentation Ölçme Yöntemleri:
Sözlü Sınav, Proje / Tasarım
16 Preparation to Final Exam Reading the lecture notes Öğretim Yöntemleri:
Soru-Cevap
17 Term Exams Reading the lecture notes Ölçme Yöntemleri:
Yazılı 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: 11.05.2024 05:53