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 2022-2023 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 Has capability in the fields of mathematics, science and computer that form the foundations of engineering 3
PLO02 Bilgi - Kuramsal, Olgusal Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, 5
PLO03 Bilgi - Kuramsal, Olgusal Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. 3
PLO04 Bilgi - Kuramsal, Olgusal Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. 3
PLO05 Bilgi - Kuramsal, Olgusal Ability to design and to conduct experiments, to collect data, to analyze and to interpret results 4
PLO06 Bilgi - Kuramsal, Olgusal Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence 3
PLO07 Beceriler - Bilişsel, Uygulamalı Can access information,gains the ability to do resource research and uses information resources 4
PLO08 Beceriler - Bilişsel, Uygulamalı Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability 3
PLO09 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language 3
PLO10 Yetkinlikler - Öğrenme Yetkinliği Professional and ethical responsibility,
PLO11 Yetkinlikler - Öğrenme Yetkinliği Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications,
PLO12 Yetkinlikler - Öğrenme Yetkinliği Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues


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: 25.11.2022 07:36